{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning for $t\\bar{t}Z$ Opposite-sign dilepton analysis \n", "This notebook uses ATLAS Open Data http://opendata.atlas.cern to show you the steps to implement Machine Learning in the $t\\bar{t}Z$ Opposite-sign dilepton analysis, following the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "The whole notebook takes a few hours to follow through.\n", "\n", "Notebooks are web applications that allow you to create and share documents that can contain for example:\n", "1. live code\n", "2. visualisations\n", "3. narrative text\n", "\n", "Notebooks are a perfect platform to develop Machine Learning for your work, since you'll need exactly those 3 things: code, visualisations and narrative text!\n", "\n", "We're interested in Machine Learning because we can design an algorithm to figure out for itself how to do various analyses, potentially saving us countless human-hours of design and analysis work.\n", "\n", "Machine Learning use within ATLAS includes: \n", "* particle tracking\n", "* particle identification\n", "* signal/background classification\n", "* and more!\n", "\n", "This notebook will focus on signal/background classification.\n", "\n", "By the end of this notebook you will be able to:\n", "1. run Boosted Decision Trees to classify signal and background\n", "2. know some things you can change to improve your Boosted Decision Tree\n", "\n", "Feynman diagram pictures are borrowed from our friends at https://www.particlezoo.net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction (from Section 1)\n", "\n", "Properties of the top quark have been explored by the\n", "Large Hadron Collider (LHC) and previous collider experiments in great detail. \n", "\n", "Other properties of the top quark are\n", "now becoming accessible, owing to the large centerof-mass energy and luminosity at the LHC.\n", "\n", "Measurements of top-quark pairs in association with a Z boson ($t\\bar{t}Z$) provide a direct probe of the\n", "weak couplings of the top quark. These couplings\n", "may be modified in the presence of physics beyond the\n", "Standard Model (BSM). Measurements of the $t\\bar{t}Z$ production cross sections, $\\sigma_{t\\bar{t}Z}$, can be used to\n", "set constraints on the weak couplings of the top quark. \n", "\n", "The production of $t\\bar{t}Z$ is often an important\n", "background in searches involving final states with multiple\n", "leptons and b-quarks. These processes also constitute an\n", "important background in measurements of the associated\n", "production of the Higgs boson with top quarks.\n", "\n", "This paper presents measurements of the $t\\bar{t}Z$ cross section using proton–proton (pp) collision data\n", "at a center-of mass energy $\\sqrt{s} = 13 TeV.\n", "\n", "The final states of top-quark pairs produced in association with a\n", "Z boson contain up to four isolated, prompt leptons. In this analysis, events with two opposite-sign\n", "(OS) leptons are considered. The dominant backgrounds\n", "in this channel are Z+jets and $t\\bar{t}$, \n", "\n", "(In this paper, lepton is used to denote electron or muon, and prompt lepton is used to denote a lepton produced in a Z or W\n", "boson decay, or in the decay of a τ-lepton which arises from a Z or W boson decay.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data and simulated samples (from Section 3)\n", "\n", "The data were collected with the ATLAS detector at a proton–proton (pp) collision\n", "energy of 13 TeV. \n", "\n", "Monte Carlo (MC) simulation samples are used to model the expected signal and background distributions\n", "in the different control, validation and signal regions described below. All samples were processed through the\n", "same reconstruction software as used for the data. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Opposite-sign dilepton analysis (from Section 5A)\n", "\n", "The OS dilepton analysis targets the $t\\bar{t}Z$ process, where both top quarks decay hadronically and the Z boson\n", "decays to a pair of leptons (electrons or muons). Events are required to have exactly two OSSF leptons.\n", "Events with additional isolated leptons are rejected. The invariant mass of the lepton pair is required to be in the Z boson mass window, |$m_{ll} − m_Z$| < 10 GeV. The leading (subleading) lepton is required to have a\n", "transverse momentum of at least 30 (15) GeV.\n", "\n", "The OS dilepton analysis is affected by large backgrounds from Z+jets or $t\\bar{t}$ production, both characterized\n", "by the presence of two leptons. In order to improve the signal-to-background ratio and constrain these\n", "backgrounds from data, three separate analysis regions are considered, depending on the number of\n", "jets ($n_{jets}$) and number of b-tagged jets ($n_{b-tags}$): 2l-Z-5j2b, 2l-Z-6j1b and 2l-Z-6j2b. The signal region\n", "requirements are summarized in Table 1 below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| Variable | 2l-Z-6j1b | 2l-Z-5j2b | 2l-Z-6j2b |\n", "|------|------|------|------|\n", "| Leptons | = 2, same flavour and opposite sign | = 2, same flavour and opposite sign | = 2, same flavour and opposite sign |\n", "| $m_{ll}$ | $|m_{ll} − m_Z |$ < 10 GeV | $|m_{ll} − m_Z |$ < 10 GeV | $|m_{ll} − m_Z |$ < 10 GeV |\n", "| $p_T$ (leading lepton) | > 30 GeV | > 30 GeV | > 30 GeV |\n", "| $p_T$ (subleading lepton) | > 15 GeV | > 15 GeV | > 15 GeV |\n", "| $n_{b-tags}$ | 1 | $\\geq$2 | $\\geq$2 |\n", "| $n_{jets}$ | $\\geq$6 | 5 | $\\geq$6 |\n", "\n", "Table 1: Summary of the event selection requirements in the OS dilepton signal regions.\n", "\n", "This is Table 2 of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "In signal region 2l-Z-5j2b, exactly five jets\n", "are required, of which at least two must be b-tagged. In 2l-Z-6j1b (2l-Z-6j2b), at least six jets are required with\n", "exactly one (at least two) being b-tagged jets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contents: \n", "\n", "[Running a Jupyter notebook](#running)
\n", "[To setup first time](#setupfirsttime)
\n", "[To setup everytime](#setupeverytime)
\n", "  [Lumi, fraction, file path](#fraction)
\n", "  [Samples to process](#samples_to_process)
\n", "  [Get data from files](#get_data_from_files)
\n", "  [Find the good jets!](#good_jets)
\n", "  [Find the good leptons!](#good_leptons)
\n", "  [Let's calculate some variables](#calc_variables)
\n", "  [Load data](#load_data)
\n", "  [Samples to plot](#samples_SR)
\n", "  [Function to plot Data/MC](#plot_data)
\n", "\n", "[Boosted Decision Tree (BDT) in 6j2b Region](#BDT_6j2b)
\n", "  [Training and Testing split](#BDT_6j2b_train_test_split)
\n", "  [Training](#BDT_6j2b_training)
\n", "  [Signal Region plot](#plot_6j2b_SR)
\n", " \n", "[Boosted Decision Tree (BDT) in 5j2b Region](#BDT_5j2b)
\n", "  [Training and Testing split](#BDT_5j2b_train_test_split)
\n", "  [Training](#BDT_5j2b_training)
\n", "  [Signal Region plot](#plot_5j2b_SR)
\n", " \n", "[Boosted Decision Tree (BDT) in 6j1b Region](#BDT_6j1b)
\n", "  [Training and Testing split](#BDT_6j1b_train_test_split)
\n", "  [Training](#BDT_6j1b_training)
\n", "  [Signal Region plot](#plot_6j1b_SR)
\n", "\n", "[Control Region plots](#data_CR)
\n", "  [6j2b Control Region plot](#plot_6j2b_CR)
\n", "  [5j2b Control Region plot](#plot_5j2b_CR)
\n", "  [6j1b Control Region plot](#plot_6j1b_CR)
\n", "\n", "[Data-driven ttbar estimate](#data_driven)
\n", "[Function to plot data from histograms](#plot_data_from_hist)
\n", "  [6j2b Signal Region plot](#6j2b_data_driven)
\n", "  [5j2b Signal Region plot](#5j2b_data_driven)
\n", "  [6j1b Signal Region plot](#6j1b_data_driven)
\n", " \n", "[BDT feature importances](#BDT_feature_importances)
\n", " \n", "[Going further](#going_further)
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running a Jupyter notebook\n", "\n", "To run the whole Jupyter notebook, in the top menu click Cell -> Run All.\n", "\n", "To propagate a change you've made to a piece of code, click Cell -> Run All Below.\n", "\n", "You can also run a single code cell, by using the keyboard shortcut Shift+Enter." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First time setup on your computer (no need on mybinder)\n", "This first cell only needs to be run the first time you open this notebook on your computer. \n", "\n", "If you close Jupyter and re-open on the same computer, you won't need to run this first cell again.\n", "\n", "If you open on mybinder, you don't need to run this cell." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pip in /Users/me338/.local/lib/python3.8/site-packages (21.0.1)\n", "Collecting uproot3\n", " Downloading uproot3-3.14.4-py3-none-any.whl (117 kB)\n", "\u001b[K |████████████████████████████████| 117 kB 2.9 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: pandas in /Users/me338/.local/lib/python3.8/site-packages (1.1.4)\n", "Collecting pandas\n", " Downloading pandas-1.2.3-cp38-cp38-macosx_10_9_x86_64.whl (10.5 MB)\n", "\u001b[K |████████████████████████████████| 10.5 MB 10.2 MB/s eta 0:00:01 |████████████▋ | 4.1 MB 10.2 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: sklearn in /Users/me338/.local/lib/python3.8/site-packages (0.0)\n", "Requirement already satisfied: numpy>=1.16.5 in /Users/me338/.local/lib/python3.8/site-packages (from pandas) (1.19.2)\n", "Requirement already satisfied: pytz>=2017.3 in /opt/anaconda3/lib/python3.8/site-packages (from pandas) (2020.1)\n", "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/anaconda3/lib/python3.8/site-packages (from pandas) (2.8.1)\n", "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n", "Requirement already satisfied: scikit-learn in /opt/anaconda3/lib/python3.8/site-packages (from sklearn) (0.23.1)\n", "Collecting awkward0\n", " Downloading awkward0-0.15.5-py3-none-any.whl (87 kB)\n", "\u001b[K |████████████████████████████████| 87 kB 12.2 MB/s eta 0:00:01\n", "\u001b[?25hCollecting uproot3-methods\n", " Downloading uproot3_methods-0.10.0-py3-none-any.whl (32 kB)\n", "Requirement already satisfied: cachetools in /Users/me338/.local/lib/python3.8/site-packages (from uproot3) (4.1.1)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn->sklearn) (2.1.0)\n", "Requirement already satisfied: scipy>=0.19.1 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn->sklearn) (1.5.0)\n", "Requirement already satisfied: joblib>=0.11 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn->sklearn) (0.16.0)\n", "Installing collected packages: awkward0, uproot3-methods, uproot3, pandas\n", " Attempting uninstall: pandas\n", " Found existing installation: pandas 1.1.4\n", " Uninstalling pandas-1.1.4:\n", " Successfully uninstalled pandas-1.1.4\n", "Successfully installed awkward0-0.15.5 pandas-1.2.3 uproot3-3.14.4 uproot3-methods-0.10.0\n" ] } ], "source": [ "import sys\n", "!{sys.executable} -m pip install --upgrade --user pip # update the pip package installer\n", "!{sys.executable} -m pip install -U uproot3 pandas sklearn --user # install required packages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## To setup everytime\n", "Cell -> Run All Below\n", "\n", "to be done every time you re-open this notebook.\n", "\n", "We're going to be using a number of tools to help us:\n", "* uproot: lets us read .root files typically used in particle physics into data formats used in Machine Learning\n", "* pandas: lets us store data as dataframes, a format widely used in Machine Learning\n", "* numpy: provides numerical calculations such as histogramming\n", "* matplotlib: common tool for making plots, figures, images, visualisations" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import pandas as pd # to store data as dataframes\n", "import numpy as np # for numerical calculations such as histogramming\n", "import uproot3 # to read .root files as dataframes\n", "import matplotlib.pyplot as plt # for plotting\n", "from matplotlib.lines import Line2D # for dashed line in legend\n", "from matplotlib.ticker import AutoMinorLocator,MaxNLocator # for minor ticks and forcing integer tick labels\n", "\n", "import infofile # local file containing cross-sections and sum of weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lumi, fraction, file path\n", "\n", "General definitions of fraction of data used, where to access the input files.\n", "\n", "The notebook will run quicker if you download the input files to your computer, rather than reading them through the web. \n", "\n", "1. download by clicking https://atlas-opendata.web.cern.ch/atlas-opendata/samples/2020/exactly2lep/\n", "2. Make a folder called exactly2lep inside notebooks-collection-opendata/13-TeV-examples/uproot_python/\n", "3. Unzip the downloaded file in \"exactly2lep\"\n", "4. Uncomment tuple_path = \"exactly2lep/\"\n", "5. Comment out tuple_path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/samples/2020/exactly2lep/\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#lumi = 0.5 # 0.5 fb-1 for data_A\n", "#lumi = 1.9 # 1.9 fb-1 for data_B\n", "#lumi = 2.9 # 2.9 fb-1 for data_C\n", "#lumi = 4.7 # 4.7 fb-1 for data_D\n", "#lumi = 9.5 # 9.5 fb-1 for data_B+C+D\n", "lumi = 10 # 10 fb-1 for data_A+B+C+D\n", "\n", "fraction = 1 # fraction of data to use, increase this for better results\n", "MC_to_data_ratio = 1 # fraction of simulated data to use, increase this for better results\n", " \n", "tuple_path = \"exactly2lep/\" # local \n", "#tuple_path = \"https://atlas-opendata.web.cern.ch/atlas-opendata/samples/2020/exactly2lep/\" # web address" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Samples to process\n", "\n", "In this notebook we only process the background samples that have a significant contribution. You can add in the uncommneted background samples later if you wish." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "samples = {\n", "\n", " 'data': {\n", " 'list' : [\n", " 'data_A', # period A from data16\n", " 'data_B', # period B from data16\n", " 'data_C', # period C from data16\n", " 'data_D' # period D from data16\n", " ] \n", " },\n", " \n", " r'$t\\bar{t}Z$' : { # ttZ(->ee) and ttZ(->μμ) signal (associated production of a top-quark pair with one vector boson)\n", " 'list' : [\n", " 'ttee',\n", " 'ttmumu'\n", " ]\n", " },\n", "\n", " r'$t\\bar{t}$' : { # ttbar semileptonic / all-leptonic\n", " 'list' : ['ttbar_lep']\n", " },\n", " \n", " 'Z' : { # Z+jets (Events containing Z bosons with associated jets) \n", " 'list' : [\n", " 'Zmumu_PTV0_70_CVetoBVeto', # Z->μμ + jets with 0 < pT(Z) < 70 GeV whilst vetoing c and b-jets\n", " 'Zmumu_PTV0_70_CFilterBVeto', # Z->μμ + jets with 0 < pT(Z) < 70 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Zmumu_PTV0_70_BFilter', # Z->μμ + jets with 0 < pT(Z) < 70 GeV and a requirement for b-jets\n", " 'Zmumu_PTV70_140_CVetoBVeto', # Z->μμ + jets with 70 < pT(Z) < 140 GeV whilst vetoing c and b-jets\n", " 'Zmumu_PTV70_140_CFilterBVeto', # Z->μμ + jets with 70 < pT(Z) < 140 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Zmumu_PTV70_140_BFilter', # Z->μμ + jets with 70 < pT(Z) < 140 GeV and a requirement for b-jets\n", " 'Zmumu_PTV140_280_CVetoBVeto', # Z->μμ + jets with 140 < pT(Z) < 280 GeV whilst vetoing c and b-jets\n", " 'Zmumu_PTV140_280_CFilterBVeto', # Z->μμ + jets with 140 < pT(Z) < 280 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Zmumu_PTV140_280_BFilter', # Z->μμ + jets with 140 < pT(Z) < 280 GeV and a requirement for b-jets\n", " 'Zmumu_PTV280_500_CVetoBVeto', # Z->μμ + jets with 280 < pT(Z) < 500 GeV whilst vetoing c and b-jets\n", " 'Zmumu_PTV280_500_CFilterBVeto', # Z->μμ + jets with 280 < pT(Z) < 500 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Zmumu_PTV280_500_BFilter', # Z->μμ + jets with 280 < pT(Z) < 500 GeV and a requirement for b-jets\n", " 'Zmumu_PTV500_1000', # Z->μμ + jets, with 500 < pT(Z) < 1000 GeV \n", " 'Zmumu_PTV1000_E_CMS', # Z->μμ + jets with 1000 GeV < pT(Z) < centre-of-mass energy\n", " 'Zee_PTV0_70_CVetoBVeto', # Z->ee + jets with 0 < pT(Z) < 70 GeV whilst vetoing c and b-jets\n", " 'Zee_PTV0_70_CFilterBVeto', # Z->ee + jets with 0 < pT(Z) < 70 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Zee_PTV0_70_BFilter', # Z->ee + jets with 0 < pT(Z) < 70 GeV and a requirement for b-jets\n", " 'Zee_PTV70_140_CVetoBVeto', # Z->ee + jets with 70 < pT(Z) < 140 GeV whilst vetoing c and b-jets\n", " 'Zee_PTV70_140_CFilterBVeto', # Z->ee + jets with 70 < pT(Z) < 140 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Zee_PTV70_140_BFilter', # Z->ee + jets with 70 < pT(Z) < 140 GeV and a requirement for b-jets\n", " 'Zee_PTV140_280_CVetoBVeto', # Z->ee + jets with 140 < pT(Z) < 280 GeV whilst vetoing c and b-jets\n", " 'Zee_PTV140_280_CFilterBVeto', # Z->ee + jets with 140 < pT(Z) < 280 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Zee_PTV140_280_BFilter', # Z->ee + jets with 140 < pT(Z) < 280 GeV and a requirement for b-jets\n", " 'Zee_PTV280_500_CVetoBVeto', # Z->μμ + jets with 280 < pT(Z) < 500 GeV whilst vetoing c and b-jets\n", " 'Zee_PTV280_500_CFilterBVeto', # Z->ee + jets with 280 < pT(Z) < 500 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Zee_PTV280_500_BFilter', # Z->ee + jets with 280 < pT(Z) < 500 GeV and a requirement for b-jets \n", " 'Zee_PTV500_1000', # Z->ee + jets with 500 < pT(Z) < 1000 GeV\n", " 'Zee_PTV1000_E_CMS', # Z->ee + jets with 1000 GeV < pT(Z) < centre-of-mass energy\n", " ]\n", " }, \n", "\n", " 'Other' : { # background with at least 2 prompt leptons, other than Z+jets and ttbar \n", " 'list' : [\n", " 'ZqqZll', # Z(->qq)Z(->ll)\n", " 'single_top_wtchan', # Wt\n", " 'single_antitop_wtchan', # Wt\n", " 'lllv', # W(->lv)Z(->ll)\n", " 'ttW',\n", " 'llll', # ZZ->llll\n", " 'llvv', # ZZ->llvv and WW->lvlv\n", " 'Ztautau_PTV0_70_CVetoBVeto', # Z->ττ + jets with 0 < pT(Z) < 70 GeV whilst vetoing c and b-jets\n", " 'Ztautau_PTV0_70_CFilterBVeto', # Z->ττ + jets with 0 < pT(Z) < 70 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Ztautau_PTV0_70_BFilter', # Z->ττ + jets with 0 < pT(Z) < 70 GeV and a requirement for b-jets\n", " 'Ztautau_PTV70_140_CVetoBVeto', # Z->μμ + jets with 70 < pT(Z) < 140 GeV whilst vetoing c and b-jets\n", " 'Ztautau_PTV70_140_CFilterBVeto', # Z->ττ + jets with 70 < pT(Z) < 140 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Ztautau_PTV70_140_BFilter', # Z->ττ + jets with 70 < pT(Z) < 140 GeV and a requirement for b-jets\n", " 'Ztautau_PTV140_280_CVetoBVeto', # Z->μμ + jets with 140 < pT(Z) < 280 GeV whilst vetoing c and b-jets\n", " 'Ztautau_PTV140_280_CFilterBVeto', # Z->ττ + jets with 140 < pT(Z) < 280 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Ztautau_PTV140_280_BFilter', # Z->ττ + jets with 140 < pT(Z) < 280 GeV and a requirement for b-jets\n", " 'Ztautau_PTV280_500_CVetoBVeto', # Z->μμ + jets with 280 < pT(Z) < 500 GeV whilst vetoing c and b-jets\n", " 'Ztautau_PTV280_500_CFilterBVeto', # Z->ττ + jets with 280 < pT(Z) < 500 GeV and a requirement for c-jets, whilst vetoing b-jets\n", " 'Ztautau_PTV280_500_BFilter', # Z->ττ + jets with 280 < pT(Z) < 500 GeV and a requirement for b-jets\n", " 'Ztautau_PTV500_1000', # Z->ττ + jets with 500 < pT(Z) < 1000 GeV\n", " 'Ztautau_PTV1000_E_CMS' # Z->ττ + jets with 1000 GeV < pT(Z) < centre-of-mass energy\n", " ] \n", " }, \n", " \n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get data from files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define function to get data from files\n", "\n", "The datasets used in this notebook have already been filtered to include exactly 2 leptons per event, so that processing is quicker." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "def get_data_from_files(samples):\n", "\n", " data = {} # define empty dictionary to hold dataframes\n", " for s in samples: # loop over samples\n", " print('Processing '+s+' samples') # print which sample\n", " frames = [] # define empty list to hold data\n", " for val in samples[s]['list']: # loop over each file\n", " if s == 'data': prefix = \"Data/\" # Data prefix\n", " else: prefix = \"MC/mc_\"+str(infofile.infos[val][\"DSID\"])+\".\" # MC prefix\n", " fileString = tuple_path+prefix+val+\".exactly2lep.root\" # file name to open\n", " temp = read_file(fileString,val) # call the function read_file defined below\n", " if 'data' in val: # processing data file\n", " print('\\t\\tnumber of events ',len(temp)) # print total number of events for file\n", " #else: # processing Monte Carlo simulation file\n", " # print('\\t\\tsum of weights ',sum(temp['totalWeight'])) # print total weight for file\n", " \n", " frames.append(temp) # append dataframe returned from read_file to list of dataframes\n", " data[s] = pd.concat(frames) # dictionary entry is concatenated dataframes\n", " \n", " return data # return dictionary of dataframes\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define function to calculate weight of MC event\n", "\n", "paper: \"Simulated events were corrected so that the object\n", "identification, reconstruction and trigger efficiencies, energy scales and energy resolutions match those\n", "determined from data control samples.\"" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def get_xsec_weight(sample): \n", " info = infofile.infos[sample] # get infofile containing cross-sections, sums of weights, dataset IDs\n", " return (lumi*1000*info[\"xsec\"]/info[\"sumw\"])/MC_to_data_ratio #*1000 to go from fb-1 to pb-1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "def calc_weight(xsec_weight,mcWeight,scaleFactor_PILEUP,scaleFactor_ELE,\n", " scaleFactor_MUON, scaleFactor_LepTRIGGER\n", " , scaleFactor_BTAG # uncomment this to get better Data vs MC match\n", " ):\n", " return xsec_weight*mcWeight*scaleFactor_PILEUP*scaleFactor_ELE*scaleFactor_MUON*scaleFactor_LepTRIGGER#*scaleFactor_BTAG" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find the good jets!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define functions to find jets passing some minimum requirements" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "def find_good_jet_indices(jet_pt,jet_jvt,\n", " jet_eta # uncomment this when you want to impose stricter requirements on jets\n", " ): # get indices of good jets\n", " good_jets = [] # list to hold whether jets are good\n", " for i in range(len(jet_pt)): # loop over jets\n", " if jet_pt[i]>25000: # paper: \"Jets are accepted if they fulfill the requirements pT > 25 GeV\"\n", " # paper: jets with pT < 60 GeV and |η| < 2.4 are required to satisfy pileup rejection criteria (JVT)\n", " #if jet_pt[i]<60000: # extra requirements for pt < 60 GeV and |η|<2.4\n", " if jet_pt[i]<60000 and abs(jet_eta[i])<2.4: # extra requirements for pt < 60 GeV and |η|<2.4\n", " if jet_jvt[i]<0.59: \n", " good_jets.append(0) # if jvt<0.59, this isn't a good jet\n", " continue # move onto next jet\n", " good_jets.append(1) # append good jet if gets here\n", " continue # move onto next jet\n", " good_jets.append(0) # if pt<25000, this isn't a good jet\n", " \n", " string_ints = [str(i) for i in good_jets] # Convert each integer to a string\n", " str_of_ints = \"\".join(string_ints) # Combine each string\n", " return str_of_ints # return list of whether jets are good\n", "\n", "# calculate number of good jets\n", "def calc_goodjet_n(good_jets): # jet indices\n", " return good_jets.count('1') # count number of 1 in good jets list\n", "\n", "# selection on number of good jets\n", "def select_good_jet_n(good_jets):\n", " return good_jets.count('1')<5 # throw away if fewer than 5 good jets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define functions to calculate number of b-jets" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "# calculate number of (good) b-jets\n", "def calc_bjet_n(jet_MV2c10,good_jets): # MV2c10 scores and jet indices\n", " bjet_n = 0 # start counter for number of b-jets\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " for i in good_jets_indices: # loop over good jets\n", " if jet_MV2c10[i]>0.6459: # for 77% b-tag efficiency from https://cds.cern.ch/record/2160731/files/ATL-PHYS-PUB-2016-012.pdf Table 2 \n", " bjet_n+=1 # increment the counter for the number of b-jets\n", " return bjet_n # return the number of b-jets\n", "\n", "# selection on number of b-jets\n", "def select_bjet_n(bjet_n):\n", " # throw away if fewer than 1 b-jets\n", " return bjet_n<1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find the good leptons!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define function to calculate dilepton invariant mass (mll)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "def calc_inv_mass_pair(pt_1,eta_1,phi_1,E_1,pt_2,eta_2,phi_2,E_2): # pt,eta,phi,energy of 2 objects\n", " px_1 = pt_1*np.cos(phi_1) # x-momentum of object 1\n", " py_1 = pt_1*np.sin(phi_1) # y-momentum of object 1\n", " pz_1 = pt_1*np.sinh(eta_1) # z-momentum of object 1\n", " px_2 = pt_2*np.cos(phi_2) # x-momentum of object 2\n", " py_2 = pt_2*np.sin(phi_2) # y-momentum of object 2\n", " pz_2 = pt_2*np.sinh(eta_2) # z-momentum of object 2\n", " sumpx = px_1 + px_2 # x-momentum of combined system\n", " sumpy = py_1 + py_2 # y-momentum of combined system\n", " sumpz = pz_1 + pz_2 # z-momentum of combined system\n", " sump = np.sqrt(sumpx**2 + sumpy**2 + sumpz**2) # total momentum of combined system\n", " sumE = E_1 + E_2 # total energy of combined system\n", " return np.sqrt(sumE**2 - sump**2)/1000 # /1000 to go from MeV to GeV\n", "\n", "# calculate dilepton invariant mass\n", "def calc_mll(lep_pt,lep_eta,lep_phi,lep_E): # lepton pt,eta,phi,energy\n", " return calc_inv_mass_pair(lep_pt[0],lep_eta[0],lep_phi[0],lep_E[0],lep_pt[1],lep_eta[1],lep_phi[1],lep_E[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define lepton selections for Opposite-sign dilepton analysis. Information is taken from Section 4. Object Reconstruction of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "# selection on nth good lepton\n", "def select_leptons(mll, lep_ptcone30, lep_pt, lep_type, lep_etcone20, lep_tracksigd0pvunbiased, lep_eta, \n", " lep_charge\n", " ,lep_z0 # uncomment this to apply stricter requirements\n", " ): # variables for good lepton\n", " \n", " # paper: \"invariant mass of the lepton pair is required to be in the Z boson mass window, |mll − mZ| < 10 GeV\"\n", " if (mll < 81.12) or (mll > 101.12): return True\n", " \n", " # paper: \"total sum of [...] transverse momenta in a surrounding cone [...] is required to be less than 6% of [...] pT\"\n", " if lep_ptcone30[0]>0.06*lep_pt[0] or lep_ptcone30[1]>0.06*lep_pt[1]: return True # bad lepton if ptcone>6%pt\n", " \n", " # paper: \"sum of [...] transverse energies [...] within a cone of size ∆Rη = 0.2 around any electron [...] is required to be less than 6% of [...] pT\"\n", " if lep_type[0]==11 and lep_etcone20[0]>0.06*lep_pt[0]: return True # bad electron if etcone>6%pt\n", " if lep_type[1]==11 and lep_etcone20[1]>0.06*lep_pt[1]: return True # bad electron if etcone>6%pt\n", " \n", " # paper: \"significance of [...] d0 is required to satisfy |d0|/σ(d0) < 5 for electrons and |d0|/σ(d0) < 3 for muons\"\n", " if lep_type[0]==13 and lep_tracksigd0pvunbiased[0]>3: return True # bad muon if σ(d0)>3\n", " if lep_type[1]==13 and lep_tracksigd0pvunbiased[1]>3: return True # bad muon if σ(d0)>3\n", " if lep_tracksigd0pvunbiased[0]>5 or lep_tracksigd0pvunbiased[1]>5: return True # bad electron if σ(d0)>5\n", " \n", " # paper Table 2: pT (leading lepton) > 30 GeV and pT (subleading lepton) > 15 GeV\n", " if lep_pt[0]<30000 or lep_pt[1]<15000: return True # minimum pt requirments on leptons\n", " \n", " # paper: \"Muons are required to have |η| < 2.5\"\n", " if abs(lep_eta[0])>2.5 or abs(lep_eta[1])>2.5: return True # bad lepton if |η|>2.5\n", " \n", " # paper: \"Opposite-sign\"\n", " if lep_charge[0]+lep_charge[1]!=0: return True # throw away when charges don't add to 0\n", " \n", " # paper: \"longitudinal impact parameter [...], z0, is required to satisfy |z0 sinθ| < 0.5 mm\"\n", " theta_i = 2*np.arctan(np.exp(-lep_eta[0])) # calculate theta angle\n", " if abs(lep_z0[0]*np.sin(theta_i))>0.5: return True # bad lepton if z0*sinθ > 0.5mm\n", " theta_i = 2*np.arctan(np.exp(-lep_eta[1])) # calculate theta angle\n", " if abs(lep_z0[1]*np.sin(theta_i))>0.5: return True # bad lepton if z0*sinθ > 0.5mm\n", " \n", " return False # don't throw away event if gets here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's calculate some variables!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define function to return which channel. 121 is ee, 169 is mumu, 143 is emu." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# return number to represent which channel \n", "def calc_channel(lep_type):\n", " return lep_type[0]*lep_type[1] \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define functions to calculate invariant mass, ∆R and pT" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "def calc_inv_mass_triplet(pt_1,eta_1,phi_1,E_1,pt_2,eta_2,phi_2,E_2,pt_3,eta_3,phi_3,E_3): # pt,eta,phi,energy of 3 objects\n", " px_1 = pt_1*np.cos(phi_1) # x-momentum of object 1\n", " py_1 = pt_1*np.sin(phi_1) # y-momentum of object 1\n", " pz_1 = pt_1*np.sinh(eta_1) # z-momentum of object 1\n", " px_2 = pt_2*np.cos(phi_2) # x-momentum of object 2\n", " py_2 = pt_2*np.sin(phi_2) # y-momentum of object 2\n", " pz_2 = pt_2*np.sinh(eta_2) # z-momentum of object 2\n", " px_3 = pt_3*np.cos(phi_3) # x-momentum of object 3\n", " py_3 = pt_3*np.sin(phi_3) # y-momentum of object 3\n", " pz_3 = pt_3*np.sinh(eta_3) # z-momentum of object 3\n", " sumpx = px_1 + px_2 + px_3 # x-momentum of combined system\n", " sumpy = py_1 + py_2 + py_3 # y-momentum of combined system\n", " sumpz = pz_1 + pz_2 + pz_3 # z-momentum of combined system\n", " sump = np.sqrt(sumpx**2 + sumpy**2 + sumpz**2) # total momentum of combined system\n", " sumE = E_1 + E_2 + E_3 # total energy of combined system\n", " return np.sqrt(sumE**2 - sump**2)/1000 # /1000 to go from MeV to GeV\n", "\n", "def calc_delta_R(eta_1,phi_1,eta_2,phi_2): # eta,phi of 2 objects\n", " delta_eta = eta_1-eta_2 # Δη between the 2 objects\n", " delta_phi = phi_1-phi_2 # Δϕ between the 2 objects\n", " if delta_phi >= np.pi: delta_phi -= 2*np.pi # use π periodicity to get number between -π and π\n", " elif delta_phi < -np.pi: delta_phi += 2*np.pi # use π periodicity to get number between -π and π\n", " return np.sqrt(delta_eta**2 + delta_phi**2) # return ∆R for this object\n", "\n", "\n", "def calc_pT_triplet(pt_1,eta_1,phi_1,pt_2,eta_2,phi_2,pt_3,eta_3,phi_3): # pt,eta,phi of 3 objects\n", " px_1 = pt_1*np.cos(phi_1) # x-momentum of object 1\n", " py_1 = pt_1*np.sin(phi_1) # y-momentum of object 1\n", " px_2 = pt_2*np.cos(phi_2) # x-momentum of object 2\n", " py_2 = pt_2*np.sin(phi_2) # y-momentum of object 2\n", " px_3 = pt_3*np.cos(phi_3) # x-momentum of object 3\n", " py_3 = pt_3*np.sin(phi_3) # y-momentum of object 3\n", " pz_3 = pt_3*np.sinh(eta_3) # z-momentum of object 3\n", " sumpx = px_1 + px_2 + px_3 # x-momentum of combined system\n", " sumpy = py_1 + py_2 + py_3 # y-momentum of combined system\n", " return np.sqrt(sumpx**2 + sumpy**2)/1000 # /1000 to go from MeV to GeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define functions to calculate variables for 2b6j BDT" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# functin to calculate pt of the lepton pair\n", "def calc_ptll(lep_pt,lep_phi): # pt of dilepton system\n", " px_0 = lep_pt[0]*np.cos(lep_phi[0]) # x-momentum of lep_0\n", " py_0 = lep_pt[0]*np.sin(lep_phi[0]) # y-momentum of lep_0\n", " px_1 = lep_pt[1]*np.cos(lep_phi[1]) # x-momentum of lep_1\n", " py_1 = lep_pt[1]*np.sin(lep_phi[1]) # y-momentum of lep_1\n", " sumpx = px_0 + px_1 # x-momentum of dilepton system\n", " sumpy = py_0 + py_1 # y-momentum of dilepton system\n", " return np.sqrt(sumpx**2 + sumpy**2)/1000 #/1000 to go from MeV to GeV \n", "\n", "# function to calculate pt of ith jet\n", "def calc_pti_jet(jet_pt,good_jets,index): # jet pt and index\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " if index0.6459)+int(jet_MV2c10[j]>0.6459)+int(jet_MV2c10[k]>0.6459)\n", " if n_bjets==1: # only 1 b-jet for top quark candidate\n", " if jet_MV2c10[i]>0.6459: # if i is the b-jet\n", " b = i # assign index i to b\n", " j1 = j # assign index j to j1\n", " j2 = k # assing index k to j2\n", " elif jet_MV2c10[j]>0.6459: # if j is is the b-jet\n", " b = j # assign index j to b\n", " j1 = i # assign index i to j1\n", " j2 = k # assign index k to j2\n", " else: # if k is the b-jet\n", " b = k # assign index k to b\n", " j1 = i # assign index i to j1\n", " j2 = j # assign index j to j2\n", " px_b = jet_pt[b]*np.cos(jet_phi[b]) # x-momentum of the b-jet\n", " py_b = jet_pt[b]*np.sin(jet_phi[b]) # y-momentum of the b-jet\n", " pz_b = jet_pt[b]*np.sinh(jet_eta[b]) # z-momentum of the b-jet\n", " px_j1 = jet_pt[j1]*np.cos(jet_phi[j1]) # x-momentum of (non-b) jet 1\n", " py_j1 = jet_pt[j1]*np.sin(jet_phi[j1]) # y-momentum of (non-b) jet 1\n", " pz_j1 = jet_pt[j1]*np.sinh(jet_eta[j1]) # z-momentum of (non-b) jet 1\n", " px_j2 = jet_pt[j2]*np.cos(jet_phi[j2]) # x-momentum of (non-b) jet 2\n", " py_j2 = jet_pt[j2]*np.sin(jet_phi[j2]) # y-momentum of (non-b) jet 2\n", " pz_j2 = jet_pt[j2]*np.sinh(jet_eta[j2]) # z-momentum of (non-b) jet 2\n", " sumpx_t = px_b + px_j1 + px_j2 # x-momentum of 3-jet system\n", " sumpy_t = py_b + py_j1 + py_j2 # y-momentum of 3-jet system\n", " sumpz_t = pz_b + pz_j1 + pz_j2 # z-momentum of 3-jet system\n", " sump_t = np.sqrt(sumpz_t**2 + sumpx_t**2 + sumpy_t**2) # total momentum of 3-jet system\n", " sumE_t = jet_E[i] + jet_E[j] + jet_E[k] # total energy of 3-jet system\n", " M_bjj = np.sqrt(sumE_t**2 - sump_t**2)/1000 # mass of 3-jet system\n", " sumpx_W = px_j1 + px_j2 # x-momentum of W candidate\n", " sumpy_W = py_j1 + py_j2 # y-momentum of W candidate\n", " sumpz_W = pz_j1 + pz_j2 # z-momentum of W candidate\n", " sump_W = np.sqrt(sumpz_W**2 + sumpx_W**2 + sumpy_W**2) # total momentum of W candidate\n", " sumE_W = jet_E[j1] + jet_E[j2] # total energy of W candidate\n", " M_W = np.sqrt(sumE_W**2 - sump_W**2)/1000 # mass of W candidate\n", " if abs(M_bjj - 172.5)<15 and abs(M_W - 80)<15: # within 15 GeV of top and W mass windows\n", " Nmbjj_top += 1 # increment counter for number of top-quark candidates\n", " return Nmbjj_top # return number of top-quark candidates\n", "\n", "# function to calculate mass of the combination between any two jets with the smallest ∆R\n", "def calc_MjjMindR(jet_pt,jet_eta,jet_phi,jet_E, # jet pt,eta,phi,energy\n", " good_jets): # jet indices\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " MindR = 99 # dummy value for smallest ∆R\n", " for jet_i in good_jets_indices[:8]: # loop over good jets\n", " for jet_j in good_jets_indices[jet_i+1:8]: # loop over remaining good jets\n", " dR = calc_delta_R(jet_eta[jet_i],jet_phi[jet_i],jet_eta[jet_j],jet_eta[jet_j]) # calculate ∆R\n", " if dR < MindR: # if this ∆R is smaller than the smallest ∆R found previously\n", " MindR = dR # set this ∆R to be the smallest ∆R\n", " i1r = jet_i # i1r is the index of the 1st jet to be considered later\n", " i2r = jet_j # i2r is the index of the 2nd jet to be considered later\n", " # use function calc_inv_mass_pair defined above to get the jet-jet invariant mass\n", " MjjMindR = calc_inv_mass_pair(jet_pt[i1r],jet_eta[i1r],jet_phi[i1r],jet_E[i1r],\n", " jet_pt[i2r],jet_eta[i2r],jet_phi[i2r],jet_E[i2r])\n", " return MjjMindR # return mass of the combination between any two jets with the smallest ∆R\n", "\n", "# function to calculate mass of the two jets with the highest b-tag weight\n", "def calc_MbbPtOrd(jet_pt,jet_eta,jet_phi,jet_E,jet_MV2c10, # jet pt,eta,phi,energy,MV2c10\n", " good_jets):\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " MV2c10_scores = {} # dictionary to hold indices and MV2c10 scores\n", " for i in good_jets_indices[:8]: # loop over good jets\n", " MV2c10_scores[i] = jet_MV2c10[i] # dictionary entry of index and MV2c10 score\n", " max_MV2c10_index = max(MV2c10_scores, key=MV2c10_scores.get) # get index of jet with maximum MV2c10 score\n", " del MV2c10_scores[max_MV2c10_index] # delete jet with maximum MV2c10 score from dictionary\n", " second_MV2c10_index = max(MV2c10_scores, key=MV2c10_scores.get) # get index of jet with second MV2c10 score\n", " # use function calc_inv_mass_pair defined above to get mass of the two jets with the highest b-tag weight\n", " return calc_inv_mass_pair(jet_pt[max_MV2c10_index],jet_eta[max_MV2c10_index],jet_phi[max_MV2c10_index],\n", " jet_E[max_MV2c10_index],jet_pt[second_MV2c10_index],jet_eta[second_MV2c10_index],\n", " jet_phi[second_MV2c10_index],jet_E[second_MV2c10_index])\n", "\n", "# function to calculate jet centrality (scalar sum of pT divided by sum of E for all jets)\n", "def calc_CentrJet(jet_pt,jet_E, # jet pt and energy\n", " good_jets): # jet indices\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " sum_pt = 0 # start counter for pt of all jets\n", " sum_E = 0 # start counter for energy of all jets\n", " for i in good_jets_indices[:8]: # loop over good jets\n", " sum_pt += jet_pt[i] # add single jet pt to sum of jet pt\n", " sum_E += jet_E[i] # add single jet energy to sum of jet energy\n", " return sum_pt/sum_E # ratio of total pt to total energy\n", "\n", "# function to calculate average ∆R for all jet pairs\n", "def calc_dRjjave_jet(jet_eta,jet_phi, # jet eta,phi\n", " good_jets): # jet indices\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " delta_R = [] # list to hold ∆R between all jet pairs\n", " for i in good_jets_indices[:8]: # loop over good jets\n", " for j in good_jets_indices[i+1:8]: # loop over remaining good jets\n", " delta_R.append(calc_delta_R(jet_eta[i],jet_phi[i],jet_eta[j],jet_phi[j])) # append ∆R for this jet pair to list\n", " return sum(delta_R)/len(delta_R) # average ∆R of all jet pairs\n", "\n", "# function to calculate maximum mass between a lepton and the tagged jet with the smallest ∆R\n", "def calc_MaxMMindRlepb(lep_pt,lep_eta,lep_phi,lep_E,jet_pt,jet_eta,jet_phi,jet_E,jet_MV2c10, # pt,eta,phi,E,MV2c10\n", " good_jets):\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " # what if the b-jet isn't one of the first 8 good jets? Use the jet within the first 8 with highes MV2c10 score\n", " if len(good_jets)>8 and jet_MV2c10[good_jets_indices[0]]<0.6459 and jet_MV2c10[good_jets_indices[1]]<0.6459 and jet_MV2c10[good_jets_indices[2]]<0.6459 and jet_MV2c10[good_jets_indices[3]]<0.6459 and jet_MV2c10[good_jets_indices[4]]<0.6459 and jet_MV2c10[good_jets_indices[5]]<0.6459 and jet_MV2c10[good_jets_indices[6]]<0.6459 and jet_MV2c10[good_jets_indices[7]]<0.6459:\n", " MV2c10_scores = {} # dictionary to hold indices and btagging scores\n", " for i in good_jets_indices[:8]: # loop over good jets \n", " MV2c10_scores[i] = jet_MV2c10[i] # dictionary entry of index and btagging score \n", " ijetr = max(MV2c10_scores, key=MV2c10_scores.get) # get index of jet with maximum btagging score\n", " MaxMMindRlepb = 0 # dummy value for maximum mass between a lepton and the tagged jet with the smallest ∆R\n", " for lep_i in [0,1]: # loop over leptons\n", " MindRlepb = 99 # dummy value for smallest ∆R\n", " for jet_j in good_jets_indices[:8]: # loop over good jets\n", " if jet_MV2c10[jet_j]>0.6459: # for 77% b-tag efficiency from https://cds.cern.ch/record/2160731/files/ATL-PHYS-PUB-2016-012.pdf Table 2\n", " dRlepb = calc_delta_R(lep_eta[lep_i],lep_phi[lep_i],jet_eta[jet_j],jet_phi[jet_j]) # get ∆R\n", " if dRlepb < MindRlepb: # if this ∆R is smaller than the smallest ∆R found previously for this lep\n", " MindRlepb = dRlepb # set this ∆R to be the minimum ∆R for this lepton\n", " ijetr = jet_j # ijetr is the index of the jet to be considered later\n", " # use function calc_inv_mass_pair to get the lep-b pair invariant mass\n", " MMindRlepb = calc_inv_mass_pair(lep_pt[lep_i],lep_eta[lep_i],lep_phi[lep_i],lep_E[lep_i],\n", " jet_pt[ijetr],jet_eta[ijetr],jet_phi[ijetr],jet_E[ijetr])\n", " if MMindRlepb > MaxMMindRlepb: # if this lep-b pair mass is bigger than the biggest previous\n", " MaxMMindRlepb = MMindRlepb # set this lep-b pair mass to be the maximum mass\n", " return MaxMMindRlepb # return maximum mass between a lepton and the tagged jet with the smallest ∆R\n", "\n", "# function to calculate First Fox-Wolfram moment\n", "def calc_H1(lep_pt,lep_eta,lep_phi,lep_E,jet_pt,jet_eta,jet_phi,jet_E, # lepton and jet pt,eta,phi,energy\n", " good_jets): # jet indices\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " E_viss = 0 # start counter for visible energy in the event\n", " H1 = 0 # start counter for H1\n", " for lep_i in range(2): # loop over leptons\n", " E_viss += lep_E[lep_i] # add energy of leptons to visible energy counter\n", " for jet_i in range(len(jet_pt)): # loop over all jets (not just good jets)\n", " E_viss += jet_E[jet_i] # add energy of jets to visible energy counter\n", " for lep_i in range(2): # loop over leptons\n", " px_i = lep_pt[lep_i]*np.cos(lep_phi[lep_i]) # x-momentum of lepton i\n", " py_i = lep_pt[lep_i]*np.sin(lep_phi[lep_i]) # y-momentum of lepton i\n", " pz_i = lep_pt[lep_i]*np.sinh(lep_eta[lep_i]) # z-momentum of lepton i\n", " for lep_j in range(lep_i,2): # loop over leptons\n", " px_j = lep_pt[lep_j]*np.cos(lep_phi[lep_j]) # x-momentum of lepton j\n", " py_j = lep_pt[lep_j]*np.sin(lep_phi[lep_j]) # y-momentum of lepton j\n", " pz_j = lep_pt[lep_j]*np.sinh(lep_eta[lep_j]) # z-momentum of lepton j\n", " numerator = px_i*px_j + py_i*py_j + pz_i*pz_j # dot product of lepton momenta\n", " H1 += numerator/(E_viss**2) # H1 calculation for this lepton pairing\n", " for jet_i in good_jets_indices[:8]: # loop over good jets\n", " px_i = jet_pt[jet_i]*np.cos(jet_phi[jet_i]) # x-momentum of jet i\n", " py_i = jet_pt[jet_i]*np.sin(jet_phi[jet_i]) # y-momentum of jet i\n", " pz_i = jet_pt[jet_i]*np.sinh(jet_eta[jet_i]) # z-momentum of jet i\n", " for jet_j in good_jets_indices[jet_i:]: # loop over good jets\n", " px_j = jet_pt[jet_j]*np.cos(jet_phi[jet_j]) # x-momentum of jet j\n", " py_j = jet_pt[jet_j]*np.sin(jet_phi[jet_j]) # y-momentum of jet j\n", " pz_j = jet_pt[jet_j]*np.sinh(jet_eta[jet_j]) # z-momentum of jet j\n", " numerator = px_i*px_j + py_i*py_j + pz_i*pz_j # dot product of jet-pair momenta\n", " H1 += numerator/(E_viss**2) # H1 calculation for this jet pairing\n", " for lep_i in range(2): # loop over leptons\n", " px_i = lep_pt[lep_i]*np.cos(lep_phi[lep_i]) # x-momentum of lepton\n", " py_i = lep_pt[lep_i]*np.sin(lep_phi[lep_i]) # y-momentum of lepton\n", " pz_i = lep_pt[lep_i]*np.sinh(lep_eta[lep_i]) # z-momentum of lepton\n", " for jet_j in good_jets_indices[:8]: # loop over jets\n", " px_j = jet_pt[jet_j]*np.cos(jet_phi[jet_j]) # x-momentum of jet\n", " py_j = jet_pt[jet_j]*np.sin(jet_phi[jet_j]) # y-momentum of jet\n", " pz_j = jet_pt[jet_j]*np.sinh(jet_eta[jet_j]) # z-momentum of jet\n", " numerator = px_i*px_j + py_i*py_j + pz_i*pz_j # dot product of lepton-jet pair momenta\n", " H1 += numerator/(E_viss**2) # H1 calcuation for this lepton-jet pairing\n", " return H1 # return H1 \n", "\n", "# function to calculate sum of jet pT , up to 6 jets\n", "def calc_HT_jet6(jet_pt, # jet pt\n", " good_jets): # jet indices\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " sum_pt = 0 # start counter for total jet pt\n", " for i in good_jets_indices[:6]: # loop over good jets, up to 6 jets \n", " sum_pt += jet_pt[i] # add individual jet pt to total jet pt\n", " return sum_pt/1000 # /1000 to go from MeV to GeV\n", "\n", "# function to calculate η of dilepton system\n", "def calc_eta_ll(lep_pt,lep_eta,lep_phi): # lepton pt,eta,phi\n", " px_0 = lep_pt[0]*np.cos(lep_phi[0]) # x-momentum of lep_0\n", " py_0 = lep_pt[0]*np.sin(lep_phi[0]) # y-momentum of lep_0\n", " pz_0 = lep_pt[0]*np.sinh(lep_eta[0]) # z-momentum of lep_0\n", " px_1 = lep_pt[1]*np.cos(lep_phi[1]) # x-momentum of lep_1\n", " py_1 = lep_pt[1]*np.sin(lep_phi[1]) # y-momentum of lep_1\n", " pz_1 = lep_pt[1]*np.sinh(lep_eta[1]) # z-momentum of lep_1\n", " sumpx = px_0 + px_1 # x-momentum of dilepton system\n", " sumpy = py_0 + py_1 # y-momentum of dilepton system\n", " sumpz = pz_0 + pz_1 # z-momentum of dilepton system\n", " sump = np.sqrt(sumpz**2 + sumpx**2 + sumpy**2) # total momentum of dilepton system\n", " return np.arctanh(sumpz/sump) # arctan between z-momentum and total momentum\n", "\n", "# function to calculate sum of the two closest 2 jet invariant masses from from jjj1 and jjj2, divided by 2\n", "def calc_MWavg(jet_pt,jet_eta,jet_phi,jet_E,jet_MV2c10, # jet pt,eta,phi,energy,MV2c10\n", " good_jets): # jet indices\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " if len(good_jets_indices)<6: return 0 # don't calculate this for 5j event\n", " b1_i = 0 # index of first jet\n", " dR1 = 99 # dummy value for min ∆R between 1st b-jet and another jet\n", " for i in good_jets_indices[:8]: # loop over good jets\n", " if i==b1_i: continue # if this jet is the 1st b-jet, move onto the next jet\n", " delta_R = calc_delta_R(jet_eta[b1_i],jet_phi[b1_i],jet_eta[i],jet_phi[i]) # ∆R between the two jets\n", " if delta_R < dR1: # if this ∆R is smaller than the smallest ∆R found previously for the 1st b-jet\n", " dR1 = delta_R # set this ∆R as the smallest ∆R for the 1st b-jet\n", " jj1_1_i = i # assign index jj1_1_i to the jet closest to the 1st b-jet\n", " dR2 = 99 # dummy value for second smallest ∆R between the 1st b-jet and another jet\n", " for i in good_jets_indices[:8]: # loop over good jets\n", " if i in [b1_i,jj1_1_i]: continue # if this jet is the 1st b-jet or the jet closest to the 1st b-jet, move on\n", " delta_R = calc_delta_R(jet_eta[b1_i],jet_phi[b1_i],jet_eta[i],jet_phi[i]) # ∆R between the two jets\n", " if delta_R < dR2: # if this ∆R is smaller than the second smallest ∆R found previously for the 1st b-jet\n", " dR2 = delta_R # set this ∆R as the second smallest ∆R for the 1st b-jet\n", " jj1_2_i = i # assign index jj1_2_i to the jet second closest to the 1st b-jet\n", " for i in good_jets_indices[:8]: # looking for the second \"b-jet\"\n", " if i in [b1_i,jj1_1_i,jj1_2_i]: continue # if this jet is in the 3-jet system of the 1st b-jet, move on\n", " b2_i = i # assign index i to the next highest pt jet\n", " break # finish this for loop\n", " dR1 = 99 # dummy value for min ∆R between 2nd b-jet and another jet\n", " for i in good_jets_indices[:8]: # loop over good jets \n", " if i in [b1_i,jj1_1_i,jj1_2_i,b2_i]: continue # if i is part of 1st b-jet system or is the 2nd b-jet, move on\n", " delta_R = calc_delta_R(jet_eta[b2_i],jet_phi[b2_i],jet_eta[i],jet_phi[i]) # ∆R between the two jets\n", " if delta_R < dR1: # if this ∆R is smaller than the smallest ∆R found previously for the 2nd b-jet\n", " dR1 = delta_R # set this ∆R as the smallest ∆R for the 2nd b-jet\n", " jj2_1_i = i # assign index jj2_1_i to the jet closest to the 2nd b-jet (if not in 1st b-jet system)\n", " dR2 = 99 # dummy value for second smallest ∆R between the 2nd b-jet and another jet\n", " for i in good_jets_indices[:8]: # loop over good jets\n", " if i in [b1_i,jj1_1_i,jj1_2_i,b2_i,jj2_1_i]: continue # if this jet has been used previously, move on\n", " delta_R = calc_delta_R(jet_eta[b2_i],jet_phi[b2_i],jet_eta[i],jet_phi[i]) # ∆R between the two jets\n", " if delta_R < dR2: # if this ∆R is smaller than the second smallest ∆R found previously for the 2nd b-jet\n", " dR2 = delta_R # set this ∆R as the second smallest ∆R for the 2nd b-jet\n", " jj2_2_i = i # assign index jj2_2_i to the jet second closest to the 2nd b-jet\n", " M1 = [] # list to hold masses of W candidates from 1st b-jet\n", " # inv mass between 1st b-jet and the jet closest to it\n", " M1.append(calc_inv_mass_pair(jet_pt[b1_i],jet_eta[b1_i],jet_phi[b1_i],jet_E[b1_i],\n", " jet_pt[jj1_1_i],jet_eta[jj1_1_i],jet_phi[jj1_1_i],jet_E[jj1_1_i]))\n", " # inv mass between 1st b-jet and the jet second closest to it\n", " M1.append(calc_inv_mass_pair(jet_pt[b1_i],jet_eta[b1_i],jet_phi[b1_i],jet_E[b1_i],\n", " jet_pt[jj1_2_i],jet_eta[jj1_2_i],jet_phi[jj1_2_i],jet_E[jj1_2_i]))\n", " # inv mass between the two jets closest to the 1st b-jet\n", " M1.append(calc_inv_mass_pair(jet_pt[jj1_1_i],jet_eta[jj1_1_i],jet_phi[jj1_1_i],jet_E[jj1_1_i],\n", " jet_pt[jj1_2_i],jet_eta[jj1_2_i],jet_phi[jj1_2_i],jet_E[jj1_2_i]))\n", " M2 = [] # list to hold masses of W candidates from 2nd b-jet\n", " # inv mass between 2nd b-jet and the jet closest to it\n", " M2.append(calc_inv_mass_pair(jet_pt[b2_i],jet_eta[b2_i],jet_phi[b2_i],jet_E[b2_i],\n", " jet_pt[jj2_1_i],jet_eta[jj2_1_i],jet_phi[jj2_1_i],jet_E[jj2_1_i]))\n", " # inv mass between 2nd b-jet and the jet second closest to it\n", " M2.append(calc_inv_mass_pair(jet_pt[b2_i],jet_eta[b2_i],jet_phi[b2_i],jet_E[b2_i],\n", " jet_pt[jj2_2_i],jet_eta[jj2_2_i],jet_phi[jj2_2_i],jet_E[jj2_2_i]))\n", " # inv mass between the two jets closest to the 2nd b-jet\n", " M2.append(calc_inv_mass_pair(jet_pt[jj2_1_i],jet_eta[jj2_1_i],jet_phi[jj2_1_i],jet_E[jj2_1_i],\n", " jet_pt[jj2_2_i],jet_eta[jj2_2_i],jet_phi[jj2_2_i],jet_E[jj2_2_i]))\n", " difM = 1000000 # dummy value for the difference between the masses of the 1st and 2nd W candidates\n", " for i in range(3): # loop over [0,1,2]\n", " for j in range(3): # loop over [0,1,2]\n", " difM12 = abs(M1[i]-M2[j]) # get absolute difference between W candidate 1 and W candidate 2\n", " if difM120.6459: #77% WP to decide if it's a b-jet\n", " return jet_pt[i]/1000 # /1000 to go from MeV to GeV\n", " # what if the b-jet isn't one of the first 8 good jets? Use the jet within the first 8 with highes MV2c10 score\n", " if len(good_jets)>8 and jet_MV2c10[good_jets_indices[0]]<0.6459 and jet_MV2c10[good_jets_indices[1]]<0.6459 and jet_MV2c10[good_jets_indices[2]]<0.6459 and jet_MV2c10[good_jets_indices[3]]<0.6459 and jet_MV2c10[good_jets_indices[4]]<0.6459 and jet_MV2c10[good_jets_indices[5]]<0.6459 and jet_MV2c10[good_jets_indices[6]]<0.6459 and jet_MV2c10[good_jets_indices[7]]<0.6459:\n", " MV2c10_scores = {} # dictionary to hold indices and btagging scores\n", " for i in good_jets_indices[:8]: # loop over good jets\n", " MV2c10_scores[i] = jet_MV2c10[i] # dictionary entry of index and btagging score\n", " max_MV2c10_index = max(MV2c10_scores, key=MV2c10_scores.get) # get index of jet with maximum btagging score\n", " return jet_pt[max_MV2c10_index]/1000 # /1000 to go from MeV to GeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define function to calculate variables for BDT in 2b5j or 1b6j signal regions" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "# function to calculate mass of the two untagged jets with the highest pT\n", "def calc_MuuPtOrd(jet_pt,jet_eta,jet_phi,jet_E,jet_MV2c10, # jet pt,eta,phi,energy,MV2c10\n", " good_jets): # jet indices\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " MV2c10_scores = {} # dictionary to hold indices and MV2c10 scores\n", " for i in good_jets_indices: # loop over good jets\n", " MV2c10_scores[i] = jet_MV2c10[i] # dictionary entry of index and MV2c10 score\n", " max_MV2c10_index = max(MV2c10_scores, key=MV2c10_scores.get) # get index of jet with maximum MV2c10 score\n", " del MV2c10_scores[max_MV2c10_index] # delete jet with maximum MV2c10 score from dictionary\n", " second_MV2c10_index = max(MV2c10_scores, key=MV2c10_scores.get) # get index of jet with second MV2c10 score\n", " for i in good_jets_indices: # loop over good jets\n", " if i==max_MV2c10_index or i==second_MV2c10_index: continue # if this jet is a b-jet, move onto next jet\n", " for j in good_jets_indices[i+1:]: # loop over remaining good jets\n", " if j==max_MV2c10_index or j==second_MV2c10_index: continue # if j is a b-jet, move onto next jet\n", " # use function calc_inv_mass_pair defined above to get mass of jet pair\n", " return calc_inv_mass_pair(jet_pt[i],jet_eta[i],jet_phi[i],jet_E[i],\n", " jet_pt[j],jet_eta[j],jet_phi[j],jet_E[j])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define function to calculate number of heavy-flavour jets" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def calc_HF_n(jet_trueflav,good_jets): # jet true flavours and jet indices\n", " good_jets_indices = [i for i,b in enumerate(good_jets) if b=='1'] # get the good jets\n", " HF_n = 0 # start counter for number of heavy-flavour jets\n", " for jet_i in good_jets_indices: # loop over good jets\n", " if jet_trueflav[jet_i]==4 or jet_trueflav[jet_i]==5: # c=4, b=5\n", " HF_n += 1 # increment counter for number of heavy-flavour jets\n", " return HF_n # return number of heavy-flavour jets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Can we process the data yet?!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define function to read files" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "def read_file(path,sample):\n", " print(\"\\tProcessing: \"+sample) # print which sample is being processed\n", " data_all = pd.DataFrame() # define empty pandas DataFrame to hold all data for this sample\n", " tree = uproot3.open(path)[\"mini\"] # open the tree called mini\n", " numevents = uproot3.numentries(path, \"mini\") # number of events\n", " if 'data' in sample: fraction_MC=fraction # process fraction*1 of measured data\n", " else: fraction_MC=fraction*MC_to_data_ratio # process fraction*MC_to_data_ratio of simulated data\n", " entrystop=numevents*fraction_MC # stop after fraction of events we want to process\n", " branches = ['lep_pt','lep_eta','lep_phi','lep_E','lep_charge','lep_type','lep_ptcone30',\n", " 'lep_etcone20','lep_tracksigd0pvunbiased','met_et',\n", " 'jet_pt','jet_eta','jet_phi','jet_E','jet_jvt','jet_MV2c10'\n", " ,'lep_z0' # uncomment this for stricter lepton requirements\n", " ]\n", " if 'data' not in sample: \n", " xsec_weight = get_xsec_weight(sample) # get cross-section weight\n", " branches.extend(['mcWeight','scaleFactor_PILEUP','scaleFactor_ELE',\n", " 'scaleFactor_MUON','scaleFactor_LepTRIGGER'\n", " ,'scaleFactor_BTAG' # uncomment this for better Data vs MC match\n", " ])\n", " if 'Zmumu' in sample or 'Zee' in sample:\n", " branches.extend(['jet_trueflav'])\n", " \n", " for data in tree.iterate(branches, \n", " outputtype=pd.DataFrame, # choose output type as pandas DataFrame\n", " entrystop=entrystop): # process up to numevents*fraction\n", " \n", " nIn = len(data.index) # number of events in this batch\n", " #print('\\t\\t initial \\t\\t\\t\\t',nIn) \n", " \n", " # get index of 0th good jet\n", " data['good_jets'] = np.vectorize(find_good_jet_indices)(data.jet_pt, data.jet_jvt \n", " ,data.jet_eta # uncomment this for stricter requirements\n", " )\n", " \n", " data['goodjet_n'] = np.vectorize(calc_goodjet_n)(data.good_jets)\n", "\n", " # select on number of good jets\n", " fail = data[ np.vectorize(select_good_jet_n)(data.good_jets) ].index # get events that fail the selection\n", " data.drop(fail,inplace=True) # drop the events that have fewer than 5 jets\n", " if len(data.index)==0: continue # move onto next batch if no events left\n", " #print('\\t\\t at least 6 jets \\t\\t\\t',len(data.index))\n", " \n", " # calculate number of b-jets at 77% Working Point using function calc_bjet_n defined above\n", " data['bjet_n'] = np.vectorize(calc_bjet_n)(data.jet_MV2c10, data.good_jets)\n", " \n", " # select on number of b-jets\n", " fail = data[ np.vectorize(select_bjet_n)(data.bjet_n) ].index # get events that fail this selection\n", " data.drop(fail,inplace=True) # drop the events with fewer than 1 b-jets\n", " if len(data.index)==0: continue # move onto next batch if no events left\n", " #print('\\t\\t at least 2 b-jets \\t\\t\\t',len(data.index))\n", " \n", " # calculation of 2-lepton invariant mass \n", " data['mll'] = np.vectorize(calc_mll)(data.lep_pt, data.lep_eta, data.lep_phi, data.lep_E)\n", " \n", " # select on whether leptons are good \n", " fail = data[ np.vectorize(select_leptons)(data.mll, data.lep_ptcone30, data.lep_pt, data.lep_type,\n", " data.lep_etcone20, data.lep_tracksigd0pvunbiased, \n", " data.lep_eta, data.lep_charge\n", " ,data.lep_z0 # uncomment this for stricter requirements\n", " ) ].index # get events that fail this selection\n", " data.drop(fail, inplace=True) # drop events where leptons aren't good\n", " if len(data.index)==0: continue # move onto next batch if no events left\n", " #print('\\t\\t leptons are good \\t\\t\\t',len(data.index))\n", " \n", " data['channel'] = np.vectorize(calc_channel)(data.lep_type) # ee, mm or em\n", "\n", " data['ptll'] = np.vectorize(calc_ptll)(data.lep_pt, data.lep_phi) # pt of dilepton system\n", " data['pt4_jet'] = np.vectorize(calc_pti_jet)(data.jet_pt, data.good_jets, 3) # pt of 4th jet\n", " data['pt6_jet'] = np.vectorize(calc_pti_jet)(data.jet_pt, data.good_jets, 5) # pt of 6th jet\n", " data['dRll'] = np.vectorize(calc_dRll)(data.lep_eta, data.lep_phi) # ∆R between the two leptons\n", " \n", " # get number of jet pairs with mass within a window of 30 GeV around 85 GeV (~Z/W mass)\n", " data['NJetPairsZMass'] = np.vectorize(calc_NJetPairsZMass)(data.jet_pt, data.jet_eta, data.jet_phi,\n", " data.jet_E, data.good_jets)\n", " \n", " # get mass of the combination between any two jets with the smallest ∆R\n", " data['MjjMindR'] = np.vectorize(calc_MjjMindR)(data.jet_pt, data.jet_eta, data.jet_phi, data.jet_E,\n", " data.good_jets)\n", " \n", " # get number of top-quark candidates\n", " data['Nmbjj_top'] = np.vectorize(calc_Nmbjj_top)(data.jet_pt, data.jet_eta, data.jet_phi, data.jet_E,\n", " data.jet_MV2c10, data.good_jets)\n", " \n", " # get mass of the two jets with the highest b-tag weight\n", " data['MbbPtOrd'] = np.vectorize(calc_MbbPtOrd)(data.jet_pt, data.jet_eta, data.jet_phi, data.jet_E,\n", " data.jet_MV2c10, data.good_jets)\n", " \n", " # get jet centrality (scalar sum of pT divided by sum of E for all jets)\n", " data['CentrJet'] = np.vectorize(calc_CentrJet)(data.jet_pt, data.jet_E, data.good_jets)\n", " \n", " # get average ∆R for all jet pairs\n", " data['dRjjave_jet'] = np.vectorize(calc_dRjjave_jet)(data.jet_eta, data.jet_phi, data.good_jets)\n", " \n", " # get maximum mass between a lepton and the tagged jet with the smallest ∆R\n", " data['MaxMMindRlepb'] = np.vectorize(calc_MaxMMindRlepb)(data.lep_pt, data.lep_eta, data.lep_phi,\n", " data.lep_E, data.jet_pt, data.jet_eta, \n", " data.jet_phi, data.jet_E, data.jet_MV2c10,\n", " data.good_jets)\n", " # get sum of jet pT , up to 6 jets\n", " data['HT_jet6'] = np.vectorize(calc_HT_jet6)(data.jet_pt, data.good_jets)\n", " \n", " data['eta_ll'] = np.vectorize(calc_eta_ll)(data.lep_pt, data.lep_eta, data.lep_phi) # η of dilepton system\n", " \n", " # get sum of the two closest 2 jet invariant masses from from jjj1 and jjj2, divided by 2\n", " data['MWavg'] = np.vectorize(calc_MWavg)(data.jet_pt, data.jet_eta, data.jet_phi, data.jet_E, \n", " data.jet_MV2c10, data.good_jets)\n", " \n", " # get ∆R between two jets with the highest b-tagging weight in the event\n", " data['dRbb'] = np.vectorize(calc_dRbb)(data.jet_eta, data.jet_phi, data.jet_MV2c10, data.good_jets)\n", " \n", " # get pT of the first b-jet (ordered according to the pT)\n", " data['pT1b'] = np.vectorize(calc_pT1b)(data.jet_pt, data.jet_MV2c10, data.good_jets)\n", " \n", " # get H1 (First Fox-Wolfram moment)\n", " data['H1'] = np.vectorize(calc_H1)(data.lep_pt, data.lep_eta, data.lep_phi, data.lep_E,\n", " data.jet_pt, data.jet_eta, data.jet_phi, data.jet_E,\n", " data.good_jets)\n", " \n", " data['pt5_jet'] = np.vectorize(calc_pti_jet)(data.jet_pt, data.good_jets, 4) # pt of 5th jet\n", " \n", " # get mass of the two untagged jets with the highest pT\n", " data['MuuPtOrd'] = np.vectorize(calc_MuuPtOrd)(data.jet_pt, data.jet_eta, data.jet_phi, data.jet_E,\n", " data.jet_MV2c10, data.good_jets)\n", " \n", " if 'data' not in sample: # if MC\n", " # calculate total weight of event from all individual weights\n", " data['totalWeight'] = np.vectorize(calc_weight)(xsec_weight,data.mcWeight,data.scaleFactor_PILEUP,\n", " data.scaleFactor_ELE,data.scaleFactor_MUON,\n", " data.scaleFactor_LepTRIGGER\n", " ,data.scaleFactor_BTAG\n", " )\n", " data.drop(['mcWeight','scaleFactor_PILEUP','scaleFactor_ELE',\n", " 'scaleFactor_MUON','scaleFactor_LepTRIGGER'\n", " ,'scaleFactor_BTAG' # uncomment this for better Data vs MC match\n", " ], axis=1, inplace=True)\n", " \n", " if 'Zmumu' in sample or 'Zee' in sample:\n", " # calculate number of heavy-flavour jets at truth level\n", " data['HF_n'] = np.vectorize(calc_HF_n)(data.jet_trueflav,data.good_jets)\n", " data.drop(['jet_trueflav'], axis=1, inplace=True)\n", "\n", " # drop variables that don't need to be saved to csv\n", " data.drop(['good_jets','lep_pt','lep_eta','lep_phi','lep_E','lep_charge','lep_type','lep_ptcone30',\n", " 'lep_etcone20','lep_tracksigd0pvunbiased','met_et','jet_pt','jet_eta','jet_phi',\n", " 'jet_E','jet_jvt','jet_MV2c10'\n", " ,'lep_z0'\n", " ], \n", " axis=1, inplace=True)\n", " \n", " nOut = len(data.index) # number of events passing selections in this batch\n", " data_all = data_all.append(data) # append dataframe from this batch to the dataframe for the whole sample\n", " \n", "\n", " return data_all # return dataframe containing events passing all selections\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is where the processing happens" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing data samples\n", "\tProcessing: data_A\n", "\t\tnumber of events 185\n", "\tProcessing: data_B\n", "\t\tnumber of events 684\n", "\tProcessing: data_C\n", "\t\tnumber of events 1007\n", "\tProcessing: data_D\n", "\t\tnumber of events 1710\n", "Processing $t\\bar{t}Z$ samples\n", "\tProcessing: ttee\n", "\tProcessing: ttmumu\n", "Processing $t\\bar{t}$ samples\n", "\tProcessing: ttbar_lep\n", "Processing Z samples\n", "\tProcessing: Zmumu_PTV0_70_CVetoBVeto\n", "\tProcessing: Zmumu_PTV0_70_CFilterBVeto\n", "\tProcessing: Zmumu_PTV0_70_BFilter\n", "\tProcessing: Zmumu_PTV70_140_CVetoBVeto\n", "\tProcessing: Zmumu_PTV70_140_CFilterBVeto\n", "\tProcessing: Zmumu_PTV70_140_BFilter\n", "\tProcessing: Zmumu_PTV140_280_CVetoBVeto\n", "\tProcessing: Zmumu_PTV140_280_CFilterBVeto\n", "\tProcessing: Zmumu_PTV140_280_BFilter\n", "\tProcessing: Zmumu_PTV280_500_CVetoBVeto\n", "\tProcessing: Zmumu_PTV280_500_CFilterBVeto\n", "\tProcessing: Zmumu_PTV280_500_BFilter\n", "\tProcessing: Zmumu_PTV500_1000\n", "\tProcessing: Zmumu_PTV1000_E_CMS\n", "\tProcessing: Zee_PTV0_70_CVetoBVeto\n", "\tProcessing: Zee_PTV0_70_CFilterBVeto\n", "\tProcessing: Zee_PTV0_70_BFilter\n", "\tProcessing: Zee_PTV70_140_CVetoBVeto\n", "\tProcessing: Zee_PTV70_140_CFilterBVeto\n", "\tProcessing: Zee_PTV70_140_BFilter\n", "\tProcessing: Zee_PTV140_280_CVetoBVeto\n", "\tProcessing: Zee_PTV140_280_CFilterBVeto\n", "\tProcessing: Zee_PTV140_280_BFilter\n", "\tProcessing: Zee_PTV280_500_CVetoBVeto\n", "\tProcessing: Zee_PTV280_500_CFilterBVeto\n", "\tProcessing: Zee_PTV280_500_BFilter\n", "\tProcessing: Zee_PTV500_1000\n", "\tProcessing: Zee_PTV1000_E_CMS\n", "Processing Other samples\n", "\tProcessing: ZqqZll\n", "\tProcessing: single_top_wtchan\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":13: RuntimeWarning: invalid value encountered in sqrt\n", " return np.sqrt(sumE**2 - sump**2)/1000 # /1000 to go from MeV to GeV\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tProcessing: single_antitop_wtchan\n", "\tProcessing: lllv\n", "\tProcessing: ttW\n", "\tProcessing: llll\n", "\tProcessing: llvv\n", "\tProcessing: Ztautau_PTV0_70_CVetoBVeto\n", "\tProcessing: Ztautau_PTV0_70_CFilterBVeto\n", "\tProcessing: Ztautau_PTV0_70_BFilter\n", "\tProcessing: Ztautau_PTV70_140_CVetoBVeto\n", "\tProcessing: Ztautau_PTV70_140_CFilterBVeto\n", "\tProcessing: Ztautau_PTV70_140_BFilter\n", "\tProcessing: Ztautau_PTV140_280_CVetoBVeto\n", "\tProcessing: Ztautau_PTV140_280_CFilterBVeto\n", "\tProcessing: Ztautau_PTV140_280_BFilter\n", "\tProcessing: Ztautau_PTV280_500_CVetoBVeto\n", "\tProcessing: Ztautau_PTV280_500_CFilterBVeto\n", "\tProcessing: Ztautau_PTV280_500_BFilter\n", "\tProcessing: Ztautau_PTV500_1000\n", "\tProcessing: Ztautau_PTV1000_E_CMS\n" ] } ], "source": [ "data_dict = get_data_from_files(samples) # process all files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Samples to plot\n", "\n", "Define dictionary of samples and colours to use for plotting" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "samples_SR = {\n", "\n", " 'data': {\n", " 'list' : ['data_A','data_B','data_C','data_D']\n", " },\n", " \n", " 'Other' : {\n", " 'color' : \"#79b278\" # purple \n", " }, \n", " \n", " 'Z + 0 HF' : {\n", " 'color' : \"#ce0000\" # light green \n", " },\n", " \n", " 'Z + 1 HF' : {\n", " 'color' : \"#ffcccc\" # middle green\n", " }, \n", " \n", " 'Z + 2 HF' : {\n", " 'color' : \"#ff6666\" # dark green\n", " }, \n", " \n", " r'$t\\bar{t}$' : {\n", " 'color' : \"#f8f8f8\" # almost white\n", " }, \n", " \n", " r'$t\\bar{t}Z$' : {\n", " 'color' : \"#00ccfd\" # blue\n", " }, \n", " \n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Regroup Z+jets into Z + 0 HF, Z + 1 HF, Z + 2 HF\n", "\n", "paper: \"The simulated Z +\n", "jets background is split into three components, Z + 0HF,\n", "Z + 1HF and Z + 2HF, depending on the number of\n", "reconstructed jets which are matched to a generator-level\n", "b- or c-hadron (heavy-flavor, or HF jets)\"" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "data_dict['Z + 0 HF'] = data_dict['Z'][data_dict['Z']['HF_n']==0] # find the events with 0 heavy-flavour partons\n", "data_dict['Z + 1 HF'] = data_dict['Z'][data_dict['Z']['HF_n']==1] # find the events with 1 heavy-flavour parton\n", "data_dict['Z + 2 HF'] = data_dict['Z'][data_dict['Z']['HF_n']>=2] # find the events with 2 heavy-flavour partons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function to plot Data and MC" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def plot_data(data, samples_to_plot=samples_SR, x_variable='BDT_6j2b_output',\n", " region_label='2L-Z-6j2b'):\n", " \n", " # *******************\n", " # general definitions (shouldn't need to change)\n", " \n", " bin_edges = np.arange(start=-1, # The interval includes this value\n", " stop=1+0.1, # The interval doesn't include this value\n", " step=0.1 ) # Spacing between values\n", " bin_centres = np.arange(start=-1+0.1/2, # The interval includes this value\n", " stop=1+0.1/2, # The interval doesn't include this value\n", " step=0.1 ) # Spacing between values\n", "\n", " mc_x = [] # define list to hold the MC histogram entries\n", " mc_weights = [] # define list to hold the MC weights\n", " mc_colors = [] # define list to hold the MC bar colors\n", " mc_labels = [] # define list to hold the MC legend labels\n", " \n", " main_axes = plt.gca() # get current axes\n", "\n", " mc_stat_err_squared = np.zeros(len(bin_centres)) # define array to hold the MC statistical uncertainties\n", " for s in samples_to_plot: # loop over samples\n", " if s!='data': # if not data\n", " mc_x.append( data[s][x_variable] ) # append to the list of Monte Carlo histogram entries\n", " totalWeights = data[s]['totalWeight'] # get the totalWeight column\n", " mc_weights.append( totalWeights ) # append to the list of Monte Carlo weights\n", " mc_colors.append( samples_to_plot[s]['color'] ) # append to the list of Monte Carlo bar colors\n", " mc_labels.append( s ) # append to the list of Monte Carlo legend labels \n", " weights_squared,_ = np.histogram(data[s][x_variable], bins=bin_edges,\n", " weights=totalWeights**2) # square the totalWeights\n", " mc_stat_err_squared = np.add(mc_stat_err_squared,weights_squared) # add weights_squared for s \n", "\n", "\n", " # plot the Monte Carlo bars\n", " mc_heights = main_axes.hist(mc_x, bins=bin_edges, \n", " weights=mc_weights, stacked=True, \n", " color=mc_colors, label=mc_labels )\n", "\n", " mc_x_tot = mc_heights[0][-1] # stacked background MC y-axis value\n", " mc_x_err = np.sqrt( mc_stat_err_squared ) # statistical error on the MC bars\n", "\n", " # histogram the data\n", " data_x,_ = np.histogram(data['data'][x_variable], bins=bin_edges ) \n", "\n", " # statistical error on the data\n", " data_x_errors = np.sqrt(data_x)\n", "\n", " # plot the data points\n", " main_axes.errorbar(x=bin_centres, \n", " y=data_x, \n", " yerr=data_x_errors,\n", " fmt='ko', # 'k' means black and 'o' is for circles \n", " label='Data')\n", "\n", " # plot the statistical uncertainty\n", " main_axes.bar(bin_centres, # x\n", " 2*mc_x_err, # heights\n", " alpha=0.5, # half transparency\n", " bottom=mc_x_tot-mc_x_err, color='none', \n", " hatch=\"////\", width=0.1, label='Stat. Unc.' )\n", "\n", " # set the x-limit of the main axes\n", " main_axes.set_xlim( left=-1, right=1 ) \n", "\n", " # separation of x axis minor ticks\n", " main_axes.xaxis.set_minor_locator( AutoMinorLocator() ) \n", "\n", " # set the axis tick parameters for the main axes\n", " main_axes.tick_params(which='both', # ticks on both x and y axes\n", " direction='in', # Put ticks inside and outside the axes\n", " top=True, # draw ticks on the top axis\n", " right=True ) # draw ticks on right axis\n", "\n", " # x-axis label\n", " main_axes.set_xlabel(x_variable, fontsize=13)\n", "\n", " # y-axis label\n", " main_axes.set_ylabel('Events / 0.1')\n", "\n", " # force y-axis ticks to be integers\n", " main_axes.yaxis.set_major_locator(MaxNLocator(integer=True))\n", "\n", " # set y-axis limits for main axes\n", " main_axes.set_ylim(bottom=0,\n", " top=np.amax(data_x)+np.sqrt(np.amax(data_x))*5 )\n", "\n", " # add minor ticks on y-axis for main axes\n", " main_axes.yaxis.set_minor_locator( AutoMinorLocator() ) \n", "\n", " # Add text 'ATLAS Open Data' on plot\n", " plt.text(0.05, # x\n", " 0.92, # y\n", " 'ATLAS Open Data', # text\n", " transform=main_axes.transAxes, # coordinate system used is that of main_axes\n", " fontsize=13 ) \n", "\n", " # Add text 'for education' on plot\n", " plt.text(0.05, # x\n", " 0.87, # y\n", " 'for education', # text\n", " transform=main_axes.transAxes, # coordinate system used is that of main_axes\n", " style='italic',\n", " fontsize=8 ) \n", "\n", " # Add energy and luminosity\n", " lumi_used = str(lumi*fraction) # luminosity to write on the plot\n", " plt.text(0.05, # x\n", " 0.81, # y\n", " '$\\sqrt{s}$=13 TeV, '+lumi_used+' fb$^{-1}$', # text\n", " transform=main_axes.transAxes ) # coordinate system used is that of main_axes\n", "\n", " # Add a label for the analysis region\n", " plt.text(0.05, # x\n", " 0.75, # y\n", " region_label, # text \n", " transform=main_axes.transAxes ) # coordinate system used is that of main_axes\n", "\n", " # draw the legend\n", " main_axes.legend(ncol=2, # number of columns\n", " frameon=False ) # no box around the legend \n", "\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## BDT in 6j2b region\n", "\n", "In order to separate signal from background, boosted decision trees (BDTs) are used. The BDTs are\n", "constructed and trained against all the contributing backgrounds, using as input 17 variables for 2l-Z-6j2b. You can see the variables used in Table 11 of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "We start by using fewer variables, you can add the other variables back in later." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Choose variables for use in BDT" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "BDT_6j2b_inputs = ['ptll','pt4_jet','pt6_jet','dRll','NJetPairsZMass','Nmbjj_top','MjjMindR','MbbPtOrd',\n", " 'CentrJet','dRjjave_jet','MaxMMindRlepb','HT_jet6','eta_ll','MWavg','dRbb','pT1b'\n", " ,'H1' # try add these variables first\n", " #,'pt5_jet','MuuPtOrd' # maybe try add these variables later?\n", " ] # variables to use in BDT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select the data in the 6j2b Signal Region." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "# define function to select only events with leptons of same type, at least 6 jets and at least 2 b-tagged jets\n", "def select_6j2b_SR(channel, goodjet_n, bjet_n):\n", " if channel==143: return True # throw away if emu\n", " if goodjet_n<6: return True # throw away if fewer than 6 jets\n", " if bjet_n<2: return True # throw away if fewer than 2 b-tagged jets\n", " return False # keep this event if it gets to this stage" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "data_6j2b_SR = {} # define empty dict\n", "for s in samples_SR:\n", " fail = data_dict[s][ np.vectorize(select_6j2b_SR)(data_dict[s]['channel'], data_dict[s]['goodjet_n'],\n", " data_dict[s]['bjet_n']) ].index # get events that fail the selection\n", " data_6j2b_SR[s] = data_dict[s].drop(fail).copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Organise data ready for BDT" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "# for sklearn data is usually organised \n", "# into one 2D array of shape (n_samples x n_features) \n", "# containing all the data and one array of categories \n", "# of length n_samples \n", "\n", "all_MC = [] # define empty list that will contain all features for the MC\n", "all_y = [] # define empty list that will contain labels whether an event in signal or background\n", "for s in samples_SR: # loop over the different keys in the dictionary of dataframes\n", " if s=='data': continue # skip data for BDT training\n", " all_MC.append(data_6j2b_SR[s][BDT_6j2b_inputs]) # append the MC dataframe to the list containing all MC features\n", " if s==r'$t\\bar{t}Z$': # only signal MC should pass this\n", " all_y.append(np.ones(data_6j2b_SR[s].shape[0])) # signal events are labelled with 1\n", " else: # only background MC should pass this\n", " all_y.append(np.zeros(data_6j2b_SR[s].shape[0])) # background events are labelled with 0\n", "X = np.concatenate(all_MC) # concatenate the list of MC dataframes into a single 2D array of features, called X\n", "y = np.concatenate(all_y) # concatenate the list of lables into a single 1D array of labels, called y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Training and Testing split (6j2b)\n", "One of the first things to do is split your data into a training and testing set. This will split your data into train-test sets: 75%-25%. It will also shuffle entries so you will not get the first 75% of X for training and the last 25% for testing. This is particularly important in cases where you load all signal events first and then the background events.\n", "\n", "Here we split our data into two independent samples. The split is to create a training and testing set. The first will be used for training the classifier and the second to evaluate its performance.\n", "\n", "We don't want to test on events that we used to train on, this prevents overfitting to some subset of data so the network would be good for the test data but much worse at any *new* data it sees." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# make train and test sets\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", " random_state=492 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training Decision Trees (6j2b)\n", "\n", "We'll use SciKit Learn (sklearn) in this tutorial. Other possible tools include keras and pytorch. \n", "\n", "After instantiating our GradientBoostingClassifier, call the fit() method with the training sample as an argument. This will train the tree, now we are ready to evaluate the performance on the held out testing set." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GradientBoostingClassifier()" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import GradientBoostingClassifier # BoostType\n", "\n", "bdt_6j2b = GradientBoostingClassifier()\n", "bdt_6j2b.fit(X_train, y_train) # fit BDT to training set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fit() method returns the trained classifier. When printed out all the hyper-parameters are listed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply the trained BDT to all Data and MC" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "bdt_outputs = bdt_6j2b.decision_function(X) # apply bdt to all X\n", "min_bdt_output = min(bdt_outputs) # get minimum BDT output score\n", "max_bdt_output = max(bdt_outputs) # get maximum BDT output score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Map the BDT decision function to the range [-1,1]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "for s in samples_SR: # loop over samples\n", " X_s = data_6j2b_SR[s][BDT_6j2b_inputs] # get the BDT input features\n", " bdt_output_on_X_s = bdt_6j2b.decision_function(X_s) # get decision function for this sample\n", " mapped_bdt_output_on_X_s = (bdt_output_on_X_s-min_bdt_output)/(max_bdt_output-min_bdt_output)*2 - 1 # map to [-1,1]\n", " data_6j2b_SR[s]['BDT_6j2b_output'] = mapped_bdt_output_on_X_s # save BDT output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6j2b Signal Region plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data() function defined [above](#plot_data) to compare Data with MC in BDT outputs in the 6j2b Signal Region. \n", "\n", "The Figure below shows the BDT output distribution in the signal\n", "region 2l-Z-6j2b." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data(data_6j2b_SR, samples_to_plot=samples_SR, x_variable='BDT_6j2b_output',\n", " region_label='2L-Z-6j2b')" ] }, { "attachments": { "6j2b_SR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 10(c) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![6j2b_SR.png](attachment:6j2b_SR.png)\n", "\n", "FIG. 10(c). The BDT distribution for the OS dilepton signal regions, (c) 2l-Z-6j2b. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons. The shaded band represents the total uncertainty. The last bin of each distribution contains the overflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## BDT in 5j2b region\n", "\n", "In order to separate signal from background, boosted decision trees (BDTs) are used. The BDTs are\n", "constructed and trained against all the contributing backgrounds, using as input 14 variables for 2l-Z-5j2b. You can see the variables used in Table 11 of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Choose variables for use in BDT" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "BDT_5j2b_inputs = ['ptll','pt4_jet','dRll','NJetPairsZMass','MjjMindR','MbbPtOrd',\n", " 'CentrJet','dRjjave_jet','HT_jet6','eta_ll','dRbb'\n", " ,'pt5_jet','MuuPtOrd','H1' # try adding these first\n", " #,'Nmbjj_top','MaxMMindRlepb','pT1b' # maybe you could also try adding these?\n", " ] # variables to use in BDT\n", "# why can't pt6_jet, MWavg be added?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select the data in the 5j2b Signal Region." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "# define function to select only events with leptons of same type, exactly 5 jets and at least 2 b-tagged jets\n", "def select_5j2b_SR(channel, goodjet_n, bjet_n):\n", " if channel==143: return True # throw away if emu\n", " if goodjet_n!=5: return True # throw away if not 5 jets\n", " if bjet_n<2: return True # throw away if fewer than 2 b-tagged jets\n", " return False # keep this event if it gets to this stage" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "data_5j2b_SR = {} # define empty dict\n", "for s in samples_SR: # loop over samples to plot in Signal Regions\n", " fail = data_dict[s][ np.vectorize(select_5j2b_SR)(data_dict[s]['channel'], data_dict[s]['goodjet_n'],\n", " data_dict[s]['bjet_n']) ].index # get events that fail the selection\n", " data_5j2b_SR[s] = data_dict[s].drop(fail).copy() # copy events that don't get dropped to a new dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Organise data ready for BDT" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "# for sklearn data is usually organised \n", "# into one 2D array of shape (n_samples x n_features) \n", "# containing all the data and one array of categories \n", "# of length n_samples \n", "\n", "all_MC = [] # define empty list that will contain all features for the MC\n", "all_y = [] # define empty list that will contain labels whether an event in signal or background\n", "for s in samples_SR: # loop over the different keys in the dictionary of dataframes\n", " if s=='data': continue # skip data for BDT training\n", " all_MC.append(data_5j2b_SR[s][BDT_5j2b_inputs]) # append the MC dataframe to the list containing all MC features\n", " if s==r'$t\\bar{t}Z$': # only signal MC should pass this\n", " all_y.append(np.ones(data_5j2b_SR[s].shape[0])) # signal events are labelled with 1\n", " else: # only background MC should pass this\n", " all_y.append(np.zeros(data_5j2b_SR[s].shape[0])) # background events are labelled with 0\n", "X = np.concatenate(all_MC) # concatenate the list of MC dataframes into a single 2D array of features, called X\n", "y = np.concatenate(all_y) # concatenate the list of lables into a single 1D array of labels, called y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Training and Testing split (5j2b)\n", "One of the first things to do is split your data into a training and testing set. This will split your data into train-test sets: 75%-25%. It will also shuffle entries so you will not get the first 75% of X for training and the last 25% for testing. This is particularly important in cases where you load all signal events first and then the background events.\n", "\n", "Here we split our data into two independent samples. The split is to create a training and testing set. The first will be used for training the classifier and the second to evaluate its performance.\n", "\n", "We don't want to test on events that we used to train on, this prevents overfitting to some subset of data so the network would be good for the test data but much worse at any *new* data it sees." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# make train and test sets\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", " random_state=492 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training Decision Trees (5j2b)\n", "\n", "We'll use SciKit Learn (sklearn) in this tutorial. Other possible tools include keras and pytorch. \n", "\n", "After instantiating our GradientBoostingClassifier, call the fit() method with the training sample as an argument. This will train the tree, now we are ready to evaluate the performance on the held out testing set." ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GradientBoostingClassifier()" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import GradientBoostingClassifier # BoostType\n", "\n", "bdt_5j2b = GradientBoostingClassifier()\n", "bdt_5j2b.fit(X_train, y_train) # fit BDT to training set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fit() method returns the trained classifier. When printed out all the hyper-parameters are listed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply the trained BDT to all Data and MC" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "bdt_outputs = bdt_5j2b.decision_function(X) # apply bdt to all X\n", "min_bdt_output = min(bdt_outputs) # get minimum BDT output score\n", "max_bdt_output = max(bdt_outputs) # get maximum BDT output score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Map the BDT decision function to the range [-1,1]" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "for s in samples_SR: # loop over samples\n", " X_s = data_5j2b_SR[s][BDT_5j2b_inputs] # get the BDT input features\n", " bdt_output_on_X_s = bdt_5j2b.decision_function(X_s) # get decision function for this sample\n", " mapped_bdt_output_on_X_s = (bdt_output_on_X_s-min_bdt_output)/(max_bdt_output-min_bdt_output)*2 - 1 # map to [-1,1]\n", " data_5j2b_SR[s]['BDT_5j2b_output'] = mapped_bdt_output_on_X_s # save BDT output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5j2b Signal Region plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data() function defined [above](#plot_data) to compare Data with MC in BDT outputs in the 5j2b Signal Region. \n", "\n", "The Figure below shows the BDT output distribution in the signal\n", "region 2l-Z-5j2b." ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data(data_5j2b_SR, samples_to_plot=samples_SR, x_variable='BDT_5j2b_output',\n", " region_label='2L-Z-5j2b')" ] }, { "attachments": { "5j2b_SR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 10(b) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![5j2b_SR.png](attachment:5j2b_SR.png)\n", "\n", "FIG. 10(b). The BDT distribution for the OS dilepton signal regions, (b) 2l-Z-5j2b. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons. The shaded band represents the total uncertainty. The last bin of each distribution contains the overflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## BDT in 6j1b region\n", "\n", "In order to separate signal from background, boosted decision trees (BDTs) are used. The BDTs are\n", "constructed and trained against all the contributing backgrounds, using as input 15 variables for 2l-Z-6j1b. You can see the variables used in Table 11 of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Choose variables for use in BDT" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "BDT_6j1b_inputs = ['ptll','pt4_jet','pt6_jet','dRll','NJetPairsZMass','MjjMindR',\n", " 'CentrJet','dRjjave_jet','MaxMMindRlepb','HT_jet6','eta_ll','MWavg','pT1b'\n", " ,'MuuPtOrd','H1' # try add these first\n", " #,'pt5_jet','Nmbjj_top','MbbPtOrd','dRbb' # maybe you could also try add these later?\n", " ] # variables to use in BDT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select the data in the 6j1b Signal Region." ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "# define function to select only events with leptons of same type, at least 6 jets and exactly 1 b-tagged jets\n", "def select_6j1b_SR(channel, goodjet_n, bjet_n):\n", " if channel==143: return True # throw away if emu\n", " if goodjet_n<6: return True # throw away if not 5 jets\n", " if bjet_n!=1: return True # throw away if fewer than 2 b-tagged jets\n", " return False # keep this event if it gets to this stage" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [], "source": [ "data_6j1b_SR = {} # define empty dict\n", "for s in samples_SR: # loop over samples to plot in Signal Regions\n", " fail = data_dict[s][ np.vectorize(select_6j1b_SR)(data_dict[s]['channel'], data_dict[s]['goodjet_n'],\n", " data_dict[s]['bjet_n']) ].index # get events that fail the selection\n", " data_6j1b_SR[s] = data_dict[s].drop(fail).copy() # copy events that don't get dropped to a new dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Organise data ready for BDT" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [], "source": [ "# for sklearn data is usually organised \n", "# into one 2D array of shape (n_samples x n_features) \n", "# containing all the data and one array of categories \n", "# of length n_samples \n", "\n", "all_MC = [] # define empty list that will contain all features for the MC\n", "all_y = [] # define empty list that will contain labels whether an event in signal or background\n", "for s in samples_SR: # loop over the different keys in the dictionary of dataframes\n", " if s=='data': continue # skip data for BDT training\n", " all_MC.append(data_6j1b_SR[s][BDT_6j1b_inputs]) # append the MC dataframe to the list containing all MC features\n", " if s==r'$t\\bar{t}Z$': # only signal MC should pass this\n", " all_y.append(np.ones(data_6j1b_SR[s].shape[0])) # signal events are labelled with 1\n", " else: # only background MC should pass this\n", " all_y.append(np.zeros(data_6j1b_SR[s].shape[0])) # background events are labelled with 0\n", "X = np.concatenate(all_MC) # concatenate the list of MC dataframes into a single 2D array of features, called X\n", "y = np.concatenate(all_y) # concatenate the list of lables into a single 1D array of labels, called y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Training and Testing split (6j1b)\n", "One of the first things to do is split your data into a training and testing set. This will split your data into train-test sets: 75%-25%. It will also shuffle entries so you will not get the first 75% of X for training and the last 25% for testing. This is particularly important in cases where you load all signal events first and then the background events.\n", "\n", "Here we split our data into two independent samples. The split is to create a training and testing set. The first will be used for training the classifier and the second to evaluate its performance.\n", "\n", "We don't want to test on events that we used to train on, this prevents overfitting to some subset of data so the network would be good for the test data but much worse at any *new* data it sees." ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# make train and test sets\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", " random_state=492 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training Decision Trees (6j1b)\n", "\n", "We'll use SciKit Learn (sklearn) in this tutorial. Other possible tools include keras and pytorch. \n", "\n", "After instantiating our GradientBoostingClassifier, call the fit() method with the training sample as an argument. This will train the tree, now we are ready to evaluate the performance on the held out testing set." ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GradientBoostingClassifier()" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import GradientBoostingClassifier # BoostType\n", "\n", "bdt_6j1b = GradientBoostingClassifier()\n", "bdt_6j1b.fit(X_train, y_train) # fit BDT to training set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fit() method returns the trained classifier. When printed out all the hyper-parameters are listed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply the trained BDT to all Data and MC" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [], "source": [ "bdt_outputs = bdt_6j1b.decision_function(X) # apply bdt to all X\n", "min_bdt_output = min(bdt_outputs) # get minimum BDT output score\n", "max_bdt_output = max(bdt_outputs) # get maximum BDT output score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Map the BDT decision function to the range [-1,1]" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [], "source": [ "for s in samples_SR: # loop over samples\n", " X_s = data_6j1b_SR[s][BDT_6j1b_inputs] # get the BDT input features\n", " bdt_output_on_X_s = bdt_6j1b.decision_function(X_s) # get decision function for this sample\n", " mapped_bdt_output_on_X_s = (bdt_output_on_X_s-min_bdt_output)/(max_bdt_output-min_bdt_output)*2 - 1 # map to [-1,1]\n", " data_6j1b_SR[s]['BDT_6j1b_output'] = mapped_bdt_output_on_X_s # save BDT output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6j1b Signal Region plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data() function defined [above](#plot_data) to compare Data with MC in BDT outputs in the 6j1b Signal Region. \n", "\n", "The Figure below shows the BDT output distribution in the signal\n", "region 2l-Z-6j1b." ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data(data_6j1b_SR, samples_to_plot=samples_SR, x_variable='BDT_6j1b_output',\n", " region_label='2L-Z-6j1b')" ] }, { "attachments": { "6j1b_SR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 10(a) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![6j1b_SR.png](attachment:6j1b_SR.png)\n", "\n", "FIG. 10(a). The BDT distribution for the OS dilepton signal regions, (a) 2l-Z-6j1b. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons. The shaded band represents the total uncertainty. The last bin of each distribution contains the overflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Control Region plots\n", "\n", "Samples containing events that pass all selections in the Control Regions." ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [], "source": [ "samples_CR = {\n", "\n", " 'data': {\n", " 'list' : ['data_A','data_B','data_C','data_D']\n", " },\n", " \n", " 'Other' : {\n", " 'color' : \"#79b278\" # green \n", " }, \n", " \n", " r'$t\\bar{t}$' : {\n", " 'color' : \"#f8f8f8\" # almost white\n", " }, \n", " \n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6j2b Control Region plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select the data in the 6j2b Control Region." ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [], "source": [ "# define function to select only events with leptons of different type, at least 6 jets and at least 2 b-tagged jets\n", "def select_6j2b_CR(channel, goodjet_n, bjet_n):\n", " if channel!=143: return True # throw away if not emu\n", " if goodjet_n<6: return True # throw away if fewer than 6 jets\n", " if bjet_n<2: return True # throw away if fewer than 2 b-tagged jets\n", " return False # keep this event if it gets to this stage" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [], "source": [ "data_6j2b_CR = {} # define empty dict\n", "for s in samples: # loop over samples\n", " fail = data_dict[s][ np.vectorize(select_6j2b_CR)(data_dict[s]['channel'], data_dict[s]['goodjet_n'],\n", " data_dict[s]['bjet_n']) ].index # get events that fail the selection\n", " data_6j2b_CR[s] = data_dict[s].drop(fail).copy() # copy events that don't get dropped to new dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Append Z and ttZ to Other as this is how the Control Region is plotted." ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [], "source": [ "data_6j2b_CR['Other'] = data_6j2b_CR['Other'].append(data_6j2b_CR['Z']) # append Z events to Other\n", "data_6j2b_CR['Other'] = data_6j2b_CR['Other'].append(data_6j2b_CR[r'$t\\bar{t}Z$']) # append ttZ events to Other" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Map the BDT decision function to the range [-1,1]" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [], "source": [ "for s in samples_CR: # loop over samples\n", " X_s = data_6j2b_CR[s][BDT_6j2b_inputs] # get the BDT input features\n", " bdt_output_on_X_s = bdt_6j2b.decision_function(X_s) # get decision function for this sample\n", " mapped_bdt_output_on_X_s = (bdt_output_on_X_s-min_bdt_output)/(max_bdt_output-min_bdt_output)*2 - 1 # map to [-1,1]\n", " data_6j2b_CR[s]['BDT_6j2b_output'] = mapped_bdt_output_on_X_s # save BDT output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data() function defined [above](#plot_data) to compare Data with MC in BDT outputs in the 6j2b Region. " ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data(data_6j2b_CR, samples_to_plot=samples_CR, x_variable='BDT_6j2b_output',\n", " region_label=r'$e\\mu$-Z-6j2b-CR')" ] }, { "attachments": { "6j2b_CR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 1(c) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![6j2b_CR.png](attachment:6j2b_CR.png)\n", "\n", "FIG. 1(c). The BDT distribution for the $t\\bar{t}$ control regions (c) 2l-Z-6j2b. The shaded band represents the\n", "total uncertainty. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons, including the $t\\bar{t}Z$ process, whose contribution is negligible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5j2b Control Region plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select the data in the 5j2b Control Region." ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [], "source": [ "# define function to select only events with leptons of different type, at least 6 jets and at least 2 b-tagged jets\n", "def select_5j2b_CR(channel, goodjet_n, bjet_n):\n", " if channel!=143: return True # throw away if not emu\n", " if goodjet_n!=5: return True # throw away if fewer than 6 jets\n", " if bjet_n<2: return True # throw away if fewer than 2 b-tagged jets\n", " return False # keep this event if it gets to this stage" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "data_5j2b_CR = {} # define empty dict\n", "for s in samples: # loop over samples\n", " fail = data_dict[s][ np.vectorize(select_5j2b_CR)(data_dict[s]['channel'], data_dict[s]['goodjet_n'],\n", " data_dict[s]['bjet_n']) ].index # get events that fail the selection\n", " data_5j2b_CR[s] = data_dict[s].drop(fail).copy() # copy events that don't get dropped to new dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Append Z and ttZ to Other as this is how the Control Region is plotted." ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "data_5j2b_CR['Other'] = data_5j2b_CR['Other'].append(data_5j2b_CR['Z']) # append Z events to Other\n", "data_5j2b_CR['Other'] = data_5j2b_CR['Other'].append(data_5j2b_CR[r'$t\\bar{t}Z$']) # append ttZ events to Other" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Map the BDT decision function to the range [-1,1]" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [], "source": [ "for s in samples_CR: # loop over samples\n", " X_s = data_5j2b_CR[s][BDT_5j2b_inputs] # get the BDT input features\n", " bdt_output_on_X_s = bdt_5j2b.decision_function(X_s) # get decision function for this sample\n", " mapped_bdt_output_on_X_s = (bdt_output_on_X_s-min_bdt_output)/(max_bdt_output-min_bdt_output)*2 - 1 # map to [-1,1]\n", " data_5j2b_CR[s]['BDT_5j2b_output'] = mapped_bdt_output_on_X_s # save BDT output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data() function defined [above](#plot_data) to compare Data with MC in BDT outputs in the 5j2b Region. " ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data(data_5j2b_CR, samples_to_plot=samples_CR, x_variable='BDT_5j2b_output',\n", " region_label=r'$e\\mu$-Z-5j2b-CR')" ] }, { "attachments": { "5j2b_CR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 1(b) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![5j2b_CR.png](attachment:5j2b_CR.png)\n", "\n", "FIG. 1(b). The BDT distribution for the $t\\bar{t}$ control regions (b) 2l-Z-5j2b. The shaded band represents the\n", "total uncertainty. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons, including the $t\\bar{t}Z$ process, whose contribution is negligible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6j1b Control Region plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select the data in the 6j1b Control Region." ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "# define function to select only events with leptons of different type, at least 6 jets and at least 2 b-tagged jets\n", "def select_6j1b_CR(channel, goodjet_n, bjet_n):\n", " if channel!=143: return True # throw away if not emu\n", " if goodjet_n<6: return True # throw away if fewer than 6 jets\n", " if bjet_n!=1: return True # throw away if fewer than 2 b-tagged jets\n", " return False # keep this event if it gets to this stage" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [], "source": [ "data_6j1b_CR = {} # define empty dict\n", "for s in samples: # loop over samples\n", " fail = data_dict[s][ np.vectorize(select_6j1b_CR)(data_dict[s]['channel'], data_dict[s]['goodjet_n'],\n", " data_dict[s]['bjet_n']) ].index # get events that fail the selection\n", " data_6j1b_CR[s] = data_dict[s].drop(fail).copy() # copy events that don't get dropped to new dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Append Z and ttZ to Other as this is how the Control Region is plotted." ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": [ "data_6j1b_CR['Other'] = data_6j1b_CR['Other'].append(data_6j1b_CR['Z']) # append Z events to Other\n", "data_6j1b_CR['Other'] = data_6j1b_CR['Other'].append(data_6j1b_CR[r'$t\\bar{t}Z$']) # append ttZ events to Other" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Map the BDT decision function to the range [-1,1]" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "for s in samples_CR: # loop over samples\n", " X_s = data_6j1b_CR[s][BDT_6j1b_inputs] # get the BDT input features\n", " bdt_output_on_X_s = bdt_6j1b.decision_function(X_s) # get decision function for this sample\n", " mapped_bdt_output_on_X_s = (bdt_output_on_X_s-min_bdt_output)/(max_bdt_output-min_bdt_output)*2 - 1 # map to [-1,1]\n", " data_6j1b_CR[s]['BDT_6j1b_output'] = mapped_bdt_output_on_X_s # save BDT output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data() function defined [above](#plot_data) to compare Data with MC in BDT outputs in the 6j1b Region. " ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data(data_6j1b_CR, samples_to_plot=samples_CR, x_variable='BDT_6j1b_output',\n", " region_label=r'$e\\mu$-Z-6j1b-CR')" ] }, { "attachments": { "6j1b_CR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 1(a) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![6j1b_CR.png](attachment:6j1b_CR.png)\n", "\n", "FIG. 1(a). The BDT distribution for the $t\\bar{t}$ control regions (a) 2l-Z-6j1b. The shaded band represents the\n", "total uncertainty. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons, including the $t\\bar{t}Z$ process, whose contribution is negligible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data-driven ttbar estimation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've so far used a Monte-Carlo method to estimate the $t\\bar{t}$ background in the OS dilepton signal regions. But we can do better.\n", "\n", "A data-driven method is used to estimate the $t\\bar{t}$ background in the OS dilepton signal regions. Control regions\n", "are defined which are identical to the signal regions, except\n", "that the requirement of two leptons with the same flavor\n", "and opposite sign is replaced by the requirement\n", "of two leptons with different flavors and opposite sign.\n", "In this manner, three regions enriched in $t\\bar{t}$ background\n", "are obtained. The number of $t\\bar{t}$ events in each sameflavor dilepton region is estimated from corresponding opposite-flavor regions, corrected for non-$t\\bar{t}$ backgrounds.\n", "\n", "This procedure is applied to each bin of the\n", "distribution under consideration." ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "def data_driven(data_SR=data_6j2b_SR, data_CR=data_6j2b_CR, x_variable='BDT_6j2b_output'):\n", " histogrammed_data = {} # dictionary to hold histogrammed data\n", " histogrammed_data_err = {} # dictionary to hold errors on histogrammed data\n", "\n", " N_tt_ll = sum(data_SR[r'$t\\bar{t}$']['totalWeight']) # number of ttbar events with opposite-sign selection\n", " N_tt_emu = sum(data_CR[r'$t\\bar{t}$']['totalWeight']) # number of ttbar events with emu selection\n", " C_tt = N_tt_ll/N_tt_emu # correction factor between opposite-sign selection and emu selection\n", " \n", " weights_squared = sum(data_SR[r'$t\\bar{t}$']['totalWeight']**2) # number of ttbar events with opposite-sign selection\n", " N_tt_ll_err = np.sqrt(weights_squared)\n", " weights_squared = sum(data_CR[r'$t\\bar{t}$']['totalWeight']**2) # number of ttbar events with opposite-sign selection\n", " N_tt_emu_err = np.sqrt(weights_squared)\n", " C_tt_err = C_tt*np.sqrt((N_tt_ll_err/N_tt_ll)**2 + (N_tt_emu_err/N_tt_emu)**2) # error propagation\n", " \n", " bin_edges = np.arange(start=-1, # The interval includes this value\n", " stop=1+0.1, # The interval doesn't include this value\n", " step=0.1 ) # Spacing between values\n", " bin_centres = np.arange(start=-1+0.1/2, # The interval includes this value\n", " stop=1+0.1/2, # The interval doesn't include this value\n", " step=0.1 ) # Spacing between values\n", "\n", " # histogram the data in the emu selection region\n", " data_x,_ = np.histogram(data_CR['data'][x_variable], bins=bin_edges)\n", " data_err = np.sqrt(data_x)\n", "\n", " # histogram the non-ttbar background in the emu selection region\n", " non_tt_background_x,_ = np.histogram(data_CR['Other'][x_variable], bins=bin_edges, weights=data_CR['Other'].totalWeight)\n", " weights_squared,_ = np.histogram(data_CR['Other'][x_variable], bins=bin_edges,\n", " weights=data_CR['Other'].totalWeight**2)\n", " non_tt_background_err = np.sqrt(weights_squared)\n", "\n", " non_corrected_tt = data_x-non_tt_background_x # data-driven ttbar in emu selection region\n", " corrected_tt = C_tt*non_corrected_tt # data-driven ttbar in ll selection region\n", " histogrammed_data[r'$t\\bar{t}$'] = corrected_tt # put in histogrammed_data\n", " \n", " # error propagation on corrected_tt = C*tt(data_x - non_tt_background_x)\n", " histogrammed_data_err[r'$t\\bar{t}$'] = np.sqrt((non_corrected_tt*C_tt_err)**2 + (C_tt*data_err)**2 + (C_tt*non_tt_background_err)**2)\n", "\n", " for s in samples_SR: # loop over samples\n", " if s!=r'$t\\bar{t}$': # everything other than ttbar\n", " if s=='data': # histogram for data\n", " histogrammed_data[s],_ = np.histogram(data_SR[s][x_variable], bins=bin_edges)\n", " else: # histogram for MC other than ttbar\n", " histogrammed_data[s],_ = np.histogram(data_SR[s][x_variable], bins=bin_edges,\n", " weights=data_SR[s].totalWeight)\n", " weights_squared,_ = np.histogram(data_SR[s][x_variable], bins=bin_edges,\n", " weights=data_SR[s].totalWeight**2)\n", " histogrammed_data_err[s] = np.sqrt(weights_squared)\n", " return histogrammed_data,histogrammed_data_err\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function to plot Data and MC from histograms" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def plot_data_from_hist(data, MC_errors, samples_to_plot=samples_SR, \n", " x_variable='BDT_6j2b_output', region_label='2L-Z-6j2b'):\n", " \n", " # *******************\n", " # general definitions (shouldn't need to change)\n", " \n", " bin_edges = np.arange(start=-1, # The interval includes this value\n", " stop=1+0.1, # The interval doesn't include this value\n", " step=0.1 ) # Spacing between values\n", " bin_centres = np.arange(start=-1+0.1/2, # The interval includes this value\n", " stop=1+0.1/2, # The interval doesn't include this value\n", " step=0.1 ) # Spacing between values\n", " \n", " main_axes = plt.gca() # get current axes\n", "\n", " mc_bottom = np.zeros(len(bin_centres)) # array to hold the bottom of the MC stack\n", " mc_stat_err_squared = np.zeros(len(bin_centres)) # define array to hold the MC statistical uncertainties\n", " for s in samples_to_plot: # loop over samples\n", " if s!='data': # if not data\n", " mc_height = main_axes.bar(bin_centres, data[s], \n", " width=0.1, bottom=mc_bottom, color=samples_to_plot[s]['color'], \n", " label=s )\n", " mc_bottom += data[s] # add this MC to the MC stack\n", " mc_stat_err_squared = np.add(mc_stat_err_squared, MC_errors[s]**2) # add weights_squared for s \n", "\n", " mc_x_tot = mc_bottom # stacked background MC y-axis value\n", " mc_x_err = np.sqrt( mc_stat_err_squared ) # statistical error on the MC bars\n", "\n", " # histogram the data\n", " data_x = data['data'] \n", "\n", " # statistical error on the data\n", " data_x_errors = np.sqrt(data_x)\n", "\n", " # plot the data points\n", " main_axes.errorbar(x=bin_centres, \n", " y=data_x, \n", " yerr=data_x_errors,\n", " fmt='ko', # 'k' means black and 'o' is for circles \n", " label='Data')\n", "\n", " # plot the statistical uncertainty\n", " main_axes.bar(bin_centres, # x\n", " 2*mc_x_err, # heights\n", " alpha=0.5, # half transparency\n", " bottom=mc_x_tot-mc_x_err, color='none', \n", " hatch=\"////\", width=0.1, label='Stat. Unc.' )\n", "\n", " # set the x-limit of the main axes\n", " main_axes.set_xlim( left=-1, right=1 ) \n", "\n", " # separation of x axis minor ticks\n", " main_axes.xaxis.set_minor_locator( AutoMinorLocator() ) \n", "\n", " # set the axis tick parameters for the main axes\n", " main_axes.tick_params(which='both', # ticks on both x and y axes\n", " direction='in', # Put ticks inside and outside the axes\n", " top=True, # draw ticks on the top axis\n", " right=True ) # draw ticks on right axis\n", "\n", " # x-axis label\n", " main_axes.set_xlabel(x_variable, fontsize=13)\n", "\n", " # y-axis label\n", " main_axes.set_ylabel('Events / 0.1')\n", "\n", " # force y-axis ticks to be integers\n", " main_axes.yaxis.set_major_locator(MaxNLocator(integer=True))\n", "\n", " # set y-axis limits for main axes\n", " main_axes.set_ylim(bottom=0,\n", " top=np.amax(data_x)+np.sqrt(np.amax(data_x))*5 )\n", "\n", " # add minor ticks on y-axis for main axes\n", " main_axes.yaxis.set_minor_locator( AutoMinorLocator() ) \n", "\n", " # Add text 'ATLAS Open Data' on plot\n", " plt.text(0.05, # x\n", " 0.92, # y\n", " 'ATLAS Open Data', # text\n", " transform=main_axes.transAxes, # coordinate system used is that of main_axes\n", " fontsize=13 ) \n", "\n", " # Add text 'for education' on plot\n", " plt.text(0.05, # x\n", " 0.87, # y\n", " 'for education', # text\n", " transform=main_axes.transAxes, # coordinate system used is that of main_axes\n", " style='italic',\n", " fontsize=8 ) \n", "\n", " # Add energy and luminosity\n", " lumi_used = str(lumi*fraction) # luminosity to write on the plot\n", " plt.text(0.05, # x\n", " 0.81, # y\n", " '$\\sqrt{s}$=13 TeV, '+lumi_used+' fb$^{-1}$', # text\n", " transform=main_axes.transAxes ) # coordinate system used is that of main_axes\n", "\n", " # Add a label for the analysis region\n", " plt.text(0.05, # x\n", " 0.75, # y\n", " region_label, # text \n", " transform=main_axes.transAxes ) # coordinate system used is that of main_axes\n", "\n", " # draw the legend\n", " main_axes.legend(ncol=2, # number of columns\n", " frameon=False ) # no box around the legend \n", "\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 6j2b Signal Region plot with data-driven $t\\bar{t}$\n", "\n", "Use the data_driven() function defined [above](#data_driven) to get the histograms of Data and MC in the 6j2b Signal Region." ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [], "source": [ "data_6j2b_SR_hists, data_6j2b_SR_errors = data_driven(data_SR=data_6j2b_SR, data_CR=data_6j2b_CR, \n", " x_variable='BDT_6j2b_output')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data_from_hist() function defined [above](#plot_data_from_hist) to compare Data with MC in BDT outputs in the 6j2b Signal Region. " ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "image/png": 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vvZcLLriAQYMGcezYMdatW8fAgQMZNmyYm/XT7t27GTp0KH379uXxxx8H4LvvvuOiiy5i4MCBfPnll8THxzNhwgSnU7vx48ezbds28vLy+POf/8yAAQO47rrrAJuVjsOL4549e3jppZf47rvv6N+/Pz/++CNXXnklYDOvPf/88+nduzfLly8HoF+/ftxzzz106dLFaYaoKIoSdKzwq2H10b59e7nttttk/vz55XUZIiIiAwcOFBGR6dOny1NPPSUiIi+88IK88847MmPGDBk7dmyJa66//nrZsmWLiIgkJydLfn6+DBw4UPbv3y9HjhyRs846S0REBg8eLCdOnBARkUGDBklRUZHcf//9snDhQhEROXnypIiIHD16VERE3njjDXn77bfll19+kb/85S8iIrJ9+3a57bbb5NChQ9K/f3/Jy8uT3NxcGTRokOTn50tcXJz88ccfsm3bNvnzn/8cUF8oilJ1mT9/vtx2220CbBULnsFhufjcoEED587i8pKfn0+dOnUAm43vG2+8Adh23tatW5d169aVWCc4efIkK1as4LbbbgPgwIED7Nixg44dO9K4cWNOnDjh3BCTl5dH7dq1ERGKioowxrBp0yaee+45AGrWrMnu3bt54IEH+P3339m1axfTpk1jw4YNdO/eHYDvv/+erl278vnnn3PNNddQt25dCgsLqVOnDlu2bOHqq6+mYcOGbN26lbPOOiug/lAUpeqSnJxMcnIyr732mv/BUXxQZaeSfvjhBzp37gzYTAFPnTpFfn4+8+bNo1+/fvz000/OB7SDo0eP0qdPHzIzM8nMzGTdunXk5uY63Qykp6fTqVMnTp486VwjWLFihdPxXW5urtONQ0FBAU8++SR33303S5YsoVmzZnTr1o0ffviBTp1sfgUdiqGgoICTJ08627j66qvZtGkTiYmJAKxfvz7gdQ1FURR/qbKKYePGjXTr1g2Ae+65hyuvvJL+/ftz33330bBhQ/Lz80tsimnUqBHt2rXjwgsv5LLLLuOjjz4iMTGRH374gUsuuYS5c+fSpUsXatWqRa1atbj33nuZOXMmXbt2BeDaa6+lV69eXHzxxezcuZMLLriA8ePHc88993Dq1CkaNWpE9+7dueuuu5g+fbpTMQwcOJAlS5Zw4YUXsnXrVm655RY2bdrkVFwbN250tqEoihJsqrS5qqKEKx988AFPPPGEW9qGDRv46KOPnAYJ5SE/P58///nPrF27lsaNGzNnzhw3lyRgc744ePBgvv/+e2ea6+7q0aNHs2LFCho0sHlXuPXWW7n77rvLLVO40rdvX44cOUJkZGSVieJmlblqWK4xKEpF852PcKvl4dxSXriGDRvGsGHDnJ/T09PJyMhg4MCBXq/JzMxk+vTpTJ8+3WuZ119/nTPPPJOff/6Z2bNn89e//tXpobUsPPPMM07LuqqKJy/Gio2wnEqywomeolQWtmzZwqRJk5g5c6ZHt+Rl4cMPP2TUqFEAXHfddSxbtqxKuy9XbFjtRC8sFYPDKik5OTnUoihKUDl16hQ33XQTzz33HG3atAm4vt27d9O6dWsAIiMjadCggTO6niu//PILiYmJzuPVV191y3/ooYecef66qK8M7Nq1yzmCcj0HuOWWW9z6pFmzZm6BscKZ5ORkhyWnJVZJOpWkBIVt27aRlpbGoUOHmDt3bqjFCVv+8Y9/EB8f74y74InevXuTn5/P0aNHOXjwoNNa7T//+Y/PqSdfnH322W4BoxybOR1U1amkZcuWsXnzZm644Qa3c4A333zTWe7XX3+lb9++AZvNV1aCOmIwxmQbYzYaY7KMMWtc0u8yxvxojNlkjHk6mDIU5+abb8YY49fhi1tvvZUmTZq4WQudOHGCXr16kZCQQHx8PI899liJ6w4cOOD2RtKyZUvnZ4fJanEGDBjA4sWL3dKef/557rjjjjLL6GDRokV06tSJ9u3b89RTT3m8/sUXX6Rz586kpKSQnZ1dJsuodu3a8frrr/tdvjqSmZnJvHnzSnXZsnr1arKyspg2bRpDhgxxBobypBRatmzJzp07AZvJ9KFDh8LS5YpZW2Tp4Q+O0J5z584lMjKSe++9l7lz55KYmOiMYwG2uA2DBg3iH//4B0OGDAlWF4Q1FTGVNEBEEh0r5caYAcBQIEFE4oEKi3K+d+9eOnXq5PfuP1+MHj2aRYsWuaXVrl2bzz77jPXr15OVlcWiRYtYtWqVW5nGjRs7f9i333479913n/NzrVq1PLZ14403Mnv2bLe02bNnc+ONN5ZZRrDt6/jLX/7CJ5984oxbvXnz5hLlXn75ZZYsWeLTHcfGjRsZPHiw2+FwA614548//uCWW27hrbfeon79+pbVO2TIEGbMmAHA3LlzueSSS0p9yaku+BPaMy8vj+TkZK6//nrGjRsXYolDRyjWGO4AnhKRfAARqbCnyJw5c0hJSXF+njFjBj179qR79+5lDnhz8cUXl5h/NMYQHR0N2OaOT506VeYfpacYvtdddx0fffSRc0SRnZ3Nnj17So1y5klGsLkbbt++Pe3ataNWrVqMGDGCDz/80K3M7bffzrZt27jyyiuZMmUKYHsDTUlJoXPnzlx33XXk5eXRrVs3Fi5c6HY0adKkTPdcHXn11VfZt28fd9xxh9u8dnksiFwZM2YMBw4coH379kyePNnraLC64iu0Z2FhISNGjOCcc87hySefDJWI4YEVfjV8vHH/CqwD1gKp9rQs4AlgNbACOK/4dT179gzUdYiIiOzfv18WLVrk/Hzvvfc6zw8fPiydO3eW/Px8ERH5448/3K696KKLJCEhocSxZMkSZ5lff/1V4uPj3a4rKCiQhIQEqVevnkyYMMGnfI899pg888wzzs+bN2+WwYMHO/0s3XHHHTJjxgwREbn66qvlf//7n4iI/Pvf/5YHHnjArz7wJON7770nY8aMcX5+6623nP6bXImLi5OcnBxnPYB88cUXIiJyyy23uMlenP3798u4ceOkXbt28q9//csvWZXqA2sKLT38IScnRxISEkqcO0hNTZVBgwbJqVOnLL7bigNYI5XAV9JFIrLbGNMEWGKM+RHbgncj4HzgPOBdY0w7+00BkJOTQ1LS6T0aqamp5Yp/kJ2dzYMPPkjv3r3Zu3evcyc02GLiHj9+nAceeIBRo0a5tQflt3GOiIggKyuL3Nxchg0b5tzd7A+uMXwBjh8/7nz7dkwnDR06lNmzZ4dk/r5169ZceOGFAIwcOZIXX3zRGW6yOI0bNy5h6aIooSQ7O5sWLVqUOAdbjPS1a9eSmZlZ6eJAp6enuy6Sx1hRZ1B7QER22//uM8Z8APQCdgHv2xXBN8aYImw3k+O4LjY21pKdiD179mT48OG89957/P7779xzzz3OvKioKL7//nun/e/YsWMZP368M9+xK7I4zz77LJdddlmpbTds2JABAwawaNEivxWDiDBq1CiPMXyHDh3Kfffdx7p168jLy6Nnz55+1ekJ1wVKsJntOfw9+aL4tJjOXSuVCdfQni+//LLzPD09nccff5y2bdu6TSl36tQp4Km9isD1xdkYs9+KOoOmGIwx9YAaInLEfn4FMAk4CgwAlhtjOgK1AEtuxhMjR45k9OjRXHTRRW6LfFu3bqVDhw6MGDGCzZs3c+LECbfryjNiyMnJoWbNmjRs2JDjx4+zZMkS/vrXv/p9/aWXXupUAE2aNOHgwYMcOXKEuLg4oqOjGTBgALfeemuJRedLL72Ut956y6+HO8B5553H1q1b+fXXX2nZsiWzZ8/m7bffLvW6HTt28PXXX9OnTx/efvvtMq/LKEoo8RXa02XCQiG4I4amwAf2t8pI4G0RWWSMqQW8YYz5HjgJjJIg/lfatWtHYWEh/fv3d0tPS0vj66+/pl69esTHx/Paa6+Vqd4bb7yRzMxM9u/fT6tWrXjiiSc477zzGDVqFIWFhRQVFXH99dczePBgv+ssLYbvjTfeyLBhw9wslIqKivj55589LjJ7knHMmDFERkby0ksvMXDgQAoLC7n11lv98t7aqVMn/vvf/3LrrbfSpUuXUs1lFcUb0jMs99YqdqqFE70vvviCCy64IGB3A+HI999/zxtvvMHkyZNDLYqiKCHGKid61UIxKIqiVAesUgxh+QqtTvSUqs4HH3zgtn8hMTGRGjVq8MknnwRU7+eff06PHj2IjIz06YrEsd/GwfTp07nzzjsBm3sM1x35Dz/8cEAyKcHHaid6OmJQFIC1a62tr4xWYw6328uXL/c65emP2+3s7GwOHz7Ms88+y5AhQ7z6O4qOjubo0aPOz9OnT2fNmjW89NJLbrEZlMqFxmNQlCqCw+32V199FfA6mCMoT1VcT1MqDlUMihJCrHa77S/Hjx93emkFOHjwoJvDuClTpjBr1iwgMC+ugeCwJMzMzKzwtqs7Vfq1YufOnQwYMIAuXboQHx/PCy+8ANicy/mafy0sLCwx/xsTE+PVNfKGDRvo06cP8fHxdOvWzbkn4qqrriI3N9erHGD78uu0WfXFX7fbiYmJjB07lvnz5zu/k8U97paFunXrOp03ZmVlMWnSJLd8V+eOoVAKwSAiIoLExETi4+NJSEjgueeeo6jIt2fW7Oxsv/b4VDWq9IghMjKS5557jh49enDkyBF69uzJ5ZdfXup1DrcWDvbu3UuvXr34xz/+UaJsQUEBI0eOZObMmSQkJHDgwAFq1qwJwMcffwzY3s48ydGlSxdrblSplDjcbq9bt85nudWrVzvLl7bGUFXIyMhg1apV5Ofn07ZtW9LS0twcYJYHhzIE2LdvHzfddBOHDx8uEXvbFYdiuOmmmwJqu7JRpUcMzZs3p0ePHgDUr1+fzp07s3v37jLV4XBT8dBDD3l0bfHpp5/SvXt3EhISAJuPoIiICMA237t///5S5Zg5cyaJiYl07drVbTemUnUJltvtqkBGRgapqank5+cDsH37dlJTU326fy8rTZo0IT09nZdeegkRITs7m759+9KjRw969OjBV199BcDDDz/MypUrSUxMZMqUKV7LVTXCUjEEw1w1Ozub7777jt69e5fpuilTphAZGcldd93lMX/Lli0YYxg4cCA9evTg6ad9xx3yJEdeXh5ZWVm8/PLL3HrrrWWST6mcBMvt9rfffkurVq147733GDdunF872sONiRMnkpeX55aWl5fHxIkTLW3H4RVh3759NGnShCVLlrBu3TrmzJnD3XffDcBTTz1F3759ycrKcrqq8VQu1FhtrhpUt9vlPaxyu+3gyJEj0qNHD5k3b56IiIwaNUree++9Uq/LysqS1q1by2+//ea1zDPPPCNt27aVnJwcOXbsmJx//vmydOlSEXF3W+1JDhGRfv36ybJly5yfW7duXcIFuKJUJ4wxApQ4jDEB1VuvXr0SaQ0aNJDffvtNcnNzZeTIkdK1a1dJSEiQunXriojI8uXL5eqrr3aW91YuXMAit9thOWKwklOnTnHttdeSkpLCNddc47WcIxD4VVddBdjWBVJSUnjllVdo2rSps5zrxqQ1a9bQqlUrLr74YmJiYoiKiuKqq67yOGfsSw71Wqoop/FmnWW11da2bduIiIigSZMmTJkyhaZNm7J+/XrWrFnjNcyuv+UqO1VaMYgIY8aMoXPnztx///0+y7755ptkZWU5F4wffPBB+vXrx9VXX+1WbtiwYU5rjaSkJAYOHMjGjRvJy8ujoKCAFStWlFhULk0Ox/TBF198QYMGDWjQwJrRoKJURtLS0oiKinJLi4qKIi0tzbI2cnJyuP3227nzzjsxxnDo0CGaN29OjRo1mDlzJoWFhYBtTdDV/b63clUOK4YdVh9WTSWtXLlSAOnWrZszAttHH30ko0aNkkaNGknLli2lZcuWcv7557tdt3v3bgHknHPOcYvedtNNN3lsZ+bMmdKlSxeJj4+Xhx56yJkeFxcn+/fv9yqHiG0q6Z577pHExESJj4+X1atXW3LvilKZmTVrltSuXVsAiYuLk1mzZgVcZ40aNSQhIUG6dOki3bt3l2eeeUYKC23R37Zs2SLdunWT7t27y4QJE5zTTidPnpQBAwZI9+7dZfLkyV7LhQtYNJWkLjGCRGFhIU2aNOG3335zmq8qiuI/usGt7FRplxgOq6Tk5GSSk5NDLU65iI+PZ+zYsaoUFKWcqELwnwULFjisONWJnqIoinKaKu12W1GqOsFyuz158mS6dOlC9+7dufTSS9m+fbvHcup2W/FF0KeSjDERwBpgt4gMNsZcCjyDTSkdBUaLyM/BlkNRfDJunLX1TZ3qM3vYsGEMGzbM+dnhdtuXXyJ/XGKce+65rFmzhqioKF555RUmTJhQrk1z9913n7rdrsZUxIjhHuAHl8+vACkikgi8Dfy9AmRQlLDF4XZ75syZAbvLHjBggNPU8/zzz2fXrl1WiKhUM4KqGIwxrYCrgWkuyQKcYT9vAOwJpgyKEs4E0+3266+/zpVXXukxz+F223E8+uijbvlTpkyxxIurUjkJ9ojheWAC4OrbdizwsTFmF3Az8FTxi3JyckhKSnIe6enpQRZTUUJDsNxuz5o1izVr1vDQQw95zK+ObrfT0tKIj4+ne/fuJCYmOr3WPv/88yV8M3nC33Ke3PoXX9OxkvT0dOezEoixos6gKQZjzGBgn4gUj5l4H3CViLQC3gQmF782NjaWNWvWOA+7cyhFqVI43G6/9NJLPsutXr2arKwspk2bxpAhQ0p9YC9dupS0tDTmz59P7dq1gyG6ZWRmZhIbG1vCNLWs6aXx9ddfs3DhQtatW8eGDRtYunQprVu3BqxXDBVNamqq81kJ7LeizmCOGC4EhhhjsoHZwCXGmI+ABBFZbS8zB7ggiDIoSlgSLLfb3333HePGjWP+/Pk0adLEsnqDQWZmJsOHD+e9995zbmYrb3pp7N27l5iYGKeijImJoUWLFrz44ovs2bOHAQMGMGDAAADuuOMOkpKSiI+P57HHHgPwWK6899y/f3+uu+46zjnnHFJSUnBsGfj222+54IILSEhIoFevXm6uOCqaoCkGEXlERFqJSFtgBPAZMBRoYIzpaC92Oe4L04pSLQiW2+2HHnqIo0ePMnz4cBITE93CdYYTViqF4cOHl9reFVdcwc6dO+nYsSPjx49nxYoVANx99920aNGC5cuXs3z5csA25bRmzRo2bNjAihUr2LBhg8dy5eW7777j+eefZ/PmzWzbto0vv/ySkydPcsMNN/DCCy+wfv16li5dSt26dQNqJyCs8KtR2gH0Bxbaz4cBG4H1QCbQrnh5q91uK4oSXsTExMjy5cvd0pYvX25JujcKCgpk+fLl8uijj0rTpk3lzTffFJGS7vFfeeUVOffcc6Vbt24SExMj77zzjsdy3hg9enQJt/7R0dFOmS+77DJn+u233y4zZ86UDRs2yAUXXODXffgCi3wlVYhLDBHJtCsBROQD4IOKaFdRlPDEqpFC8XRfRERE0L9/f/r370+3bt2YMWMGo0ePdivz66+/8uyzz/Ltt99y5plnMnr0aGcMd39p3Lgxf/zxh/PzwYMHiYk5vSbsuu4TERFBQUFBmeqvCHTns6IoFU5FK4WffvqJrVu3Oj9nZWURFxcHuLvWPnz4MPXq1aNBgwb8/vvvbjvRi7vg9nVvc+bMccZqmD59eqnrEp06dWLv3r18++23ABw5ciSkCkOd6CmKEjIqQikAHD16lLvuuovc3FwiIyNp37690ww+NTWVQYMGOdcQzj33XM455xxat27NhRde6KyjeLmxY8dy++23O8xEnQwePJi1a9fSs2dPIiIiOPvss3n11Vd9ylerVi3mzJnDXXfdxfHjx6lbty5Lly7l8OHDjB071hknxhvqRE9RFEXxiDrRUxRFUYKCKgZFURTFDVUMiqIoihuqGBRFURQ3wlIxOKyS7KvsiqIoig8WLFjg8CmnVkmKoijKadQqSVEURQkKqhgUxUIcLhcUpTKjikFRFEVxQxWDoiiK4ka5FIMx5nKrBVEURVHCg/KOGF63VIpiqLmqogSGrnVUL6w2V/XqXdUYM99bFtDYisa90aBBA6fnQ0VRFMU3Dk/Ur7322iEr6vPldrsvMBI4WizdAL2saFxRFEUJP3wphlVAnoisKJ5hjPnJ3waMMRHAGmC3iAw2xpwFzMY26lgL3CwiJ8smtqIoihIsvK4xiMiVIuIx6rWIXFyGNu4BfnD5/B9gioi0B/4AxpShLkVRFCXIBNVc1RjTCrgamGb/bIBLgLn2IjOAPwVTBkVRFKVslNdc1d+V4eeBCUCR/XNjIFdEHMFMdwEti1+Uk5NDUlKS89CFaEVRFM+kp6c7n5VAjBV1ljfm89TSChhjBgP7RGStMaZ/WSqPjY1FnegpiqKUTmpqqsNUFWPMfivqLJdiEJG1fhS7EBhijLkKqAOcAbwANDTGRNpHDa2A3eWRQVEURQkOXqeSjDENjDFPGWN+NMYcNMYcMMb8YE9rWFrFIvKIiLQSkbbACOAzEUkBlgPX2YuNAj4M/DYURVEUq/C1xvAuNquh/iLSSEQaAwPsae8G0OZfgfuNMT9jW3MI6i5qRVHKju6crt74mkpqKyL/cU0Qkd+A/xhjbi1LIyKSCWTaz7ehG+QURVHCFl8jhu3GmAnGmKaOBGNMU2PMX4GdwRdNURRFCQW+FMMN2KZ6VtjXGA5ie+tvBFwfTKHUiZ5SGcnIyGDVqlWsWLGCtm3bkpGREWqRlGqCxnxWlDAkIyOD1NRU8vLynGlRUVGkp6eTkpJS4fI41gcyMzNDcr0SGjTms6KEERMnTnRTCgB5eXlMnDgxRBIpSvlRxaAoFrBjx44ypQcTndJSAsXXPoYWFSmIolRm2rRpU6b0YOGY0srPzwdg+/btpKamqnJQyoSvEcM0Y8wq+4a2/saY8rrPUJQqT1paGlFRUW5pUVFRpKWlVagcOqWlWIEvt9tXAf2xWSINA1YZY943xqQaY4L6GqRWSUplIyUlhfT0dGrXrg1AXFxcSBaew2lKS6k4QmqVZA+ycyUwCGgmIkHZqKZWSUplJdTWPG3btmX79u0l0uPi4sjOzva7nlDfh1I+QmKVJCK/isjLIjIEuCjQxhVFKUkg7ijCZUpLqdyU2ypJw3EqSvgRLlNaSuVGF5QVpYqRkpLCa6+9BuhUkFI+yjRiMMacaYzpHixhFKW6EBsbW+KhnZmZSWxsLLm5uR7T9SGvVBSlKgZjTKYx5gxjTCNgHfCaMWZy8EVTlKrLe++957aOkJmZyfDhw3nvvfdo2LChx3R1g61UFP6MGBqIyGHgGuAtEekNXBZModRcVanqeFMK/qYHE905Xfmw2lwVEfF5ABuB5sCnwHn2tA2lXRfI0bNnT1GUyki/fv2kX79+fpdfvny5xMTEyPLly93qSEhIKJHuWt5qORzMmjVLoqKiBHAeUVFRMmvWrDLXpVQ8wBqx4Bnsz4jhCWAx8LOIfGuMaQdsLe0iY0wdY8w3xpj1xphNxpgn7OkZxpifjDHfG2PeMMbULIc+UxTLqeioZd5GBLm5uWzatMnnCCJY6M5pBfybStorIt1FZDw4I7D5s8aQD1wiIglAIjDIGHM+kAGcA3QD6gJjyyO4olRmfE0Tbdq0ifj4eL+mlaxGd04r4J9i+D8/09ywj2yO2j/WtB8iIh+7DHu+AVr5La2iVBF8rSnEx8eHbAE6XJwBKqHFl3fVPsaYB4BYY8z9LsfjQIQ/lRtjIowxWcA+YImIrHbJqwncDCwK5AYUpTISrlZJod457c0011e6Yj2+Rgy1gGhsm+DquxyHgev8qVxECkUkEduooJcxpqtL9svA5yKysvh1OTk5JCUlOY/09HS/bkZRKgvhapUUyp3T5emH4cOHB12ucCc9Pd35rARiLKm0tNVpIM6KVW7gUeBB+/ljwP+AGp7KqlWSEgrKa8kTSB3hZpVk1fVlxVM/lCe9uoNFVkn+uMSobYxJB9ri4kJDRC7xdZExJhY4JSK5xpi6wOXAf4wxY4GBwKUiUlQ2NaYoVQOztgjWZMLDN8BTcxhQ/2JYa/857MmFbZvgv4vd013KVyXKO1LQTX/Bwx/F8B7wKjANKCxD3c2BGcaYCGxTVu+KyEJjTAGwHfjaGAPwvohMKpvYilLJcX3IJ/V3T9+2CdrFl0z3VD6M8dd1tyqF8MMfxVAgIq+UtWIR2QCc6yFdHfcpVRa/5/+9KYX7hoIxsHU9DD4L/pIGsS0qnVIoC6oUwg9/zFUXGGPGG2OaG2MaOY6gS6YoVRlvSqHwFBScsqX9tgOeHGtLr6JKAcJ3Ib4648/b+yj734dc0gRoZ704ilI9ODH6QsCmAFasWEHK31OoUT+Kffv2uRc8mU+TJg3YUaw8XFGh8lYE5V1ryMnJqXhhqzilKgYROasiBHHF4UQvOTmZ5OTkim5eqaRUxnCUK1asICUlhYyMDAYNGuSxjOuDz1G+tIdhZeoDCGwBWrE50bM7HbXEiZ4/brejjDF/t1smYYzpYIwZbEXj3mjQoAHp6emqFJQqjatS6NevH61bt/ZYzpHuWr4qoVZJgZOcnOzY73XIivr8WWN4EzgJXGD/vBv4pxWNK0p1pbhSAJg0aZJzY5mDqKgoJk2a5LF8VUGVQvjhj2I4W0Sexj7BKSJ5gAmqVEq1o6I9mxanomMQeHrIN2/enJo1a1Kzps3hcJs2bXj55Zdp3rx5lVUKoFZJ4Yg/iuGkfYOaABhjzsbmOVVRqgQZGRmkpqaSn2/7Wm/fvp3U1NSgKofiD3nHiGDevHmcf/759O3bly1btlR5pQBqlRSO+KMYHsfm6K61MSYDWAZMCKZQilKRhCIGgSel4E1ZeEq3Al+O6b788ssKiz1t1hbZjqmfMWDYcPb/07YT3N90xXr8sUr61BizFjgf2xTSPSKyP5hCqVWSUpGEOgZBv3792LVrV5nSrSCsXH/72gleWroSEqukBdiMpjNFZGGwlQKoVZJSsVTXGASBuv62bOQQiFKoopv+ykoorJKeBfoCm40xc40x1xlj6ljRuKKEA6GOQRAqfM3hZ2ZmOo+gu7xWpRB2lKoYRGSF2MJ6tgOmAtdjC7yjKFWCUMYgCAeCsbmsTFZeqhTCDn9GDNitkq4FbgfOA2YEUyhFqWhSUlI4//zz6devH9nZ2X4rhfJEHIuNjbVI6sAJxuayMlt5BaIU1mT6fa9KGSgtYAPwLpCNzfX2ALwE17Hy0EA91Y9QBMkJ9PpAgsucOHHC69G3b1/p27evzzJWYFVwnOLpcXFxgs283e2Ii4vzWJ41hbbj1WVCwxjbX0eaH+nKabAoUI8/imEgEGFFY/4e7du3l9tuu03mz59vba8pYUtlUwyBPjzDQTEEQymIiBhjPCoGY4zH8oEoBV5dZklfVHbmz58vt912mwBbJZiKAZjgcj68WN6/rGjc26EjhupHoA/1WbNmSe3atZ1vprNmzQqaDFY8PMNBMQRDKYiING3a1KNiaNq0qcfygSgF1hRa0hdVhaCPGIB1ns49fbb6UMVQ/QhEMcyaNUuioqLcHkJRUVFlVg7+ymDFwzMcFEN5HsLF0z3db3R0tFNJO47atWtLdHS0x/I+2+2QIPTo51Me5TQVoRi+83Tu6bOX61sDy4HNwCZsG+Nc8x+wf2liil+riqH6EYhiKG1O22oZrHjTtkIxBPqGH6hSKD6N41q/6wiuadOmXpVCTEyMLF682O3eFi9e7Ex37QvXdKuVZFUh7EcM2GI+97Cf1we2AF3ktNJYjC32syqGak6g00C+5rTLQnmUU3kfzoEqBiumfQJVCq5v657q79evnyQkJJRpIb74w9/RF96UwuLFi8v0/6rqVIRiKAQOA0eAAvu54/OpMjcEHwKX28/nAgl2aydVDNUYK6aByjpi8PbwTEhIkMjIyKDNvXt7GJbnsEKeQJWCQzEE2p/elIJDMXTv3t3nyEI5TYVZJVnSCLQFdgBnAEOBF+zpqhiqOVZMA5VFufh6eEZGRkpCQkKp5a140w5UMVihpAJVCqwptKQ/w2G9papQaRQDEA2sBa4BooDVQAPxoRjatGkjPXv2dB5Tp04NQhcq4YBV00D+Tkf5engmJCS4TSUF8007UMXgSf6yKotAlQKvLrOkP1UxBMbUqVOdz0ogW8JdMQA17WsJ99s/d8PmTiPbfhTYRxLNXK/TEUP1waqFYxH/1gh8PTxdry/toeprwdSfdKsUQyDTWVZYJflM79HvtEWRj/JWKAarpvsqO2E/YsDmovst4HkfZXQqqZpjlampSOA7lx3XB1spWKEkPMnv7b68pQcivyPdp7JwKIZSlEigikGVwmkqg2K4yP5D3wBk2Y+ripVRxaBYsjlNJPCdy1ZY0fibHqhisGo6KxClcOLECd/t9uhn24dQijyBKgZVCqcJe8UQyKGKofpRkS4xwuFNO2DFYMG0T6AyOBWDt3Y7JAgRkQEtxPs7Ygj0/1tVUMWgVCkqSjGEy5u2JSOGANcCLJPDW7sRkTblEMBCfFkXn8ur9KsKVimGUkN7hgIN7akEDYuCwpQnHKen9HITqKvqh2+A0RbI46v+dvFQv2Hp8lhEMOJKVBasDu0Z8tGBp0NHDNWPihoxhMPCsWVTOAFaDQV95BKgVdL06dOda09t2rSR6dOnex0x6AK0DXQqSalKVJRiCFQpWLFwbNUR6uksNwVlsVXS9OnTPVqreVIOqhROo4pBqVJUtGIIh4dqZVcKborBYqukNm3aeNzf0qZNmxJlVSmcxirFYGx1hRdJSUmyZs2aUIuhVCCOud/ioTCtriM/P58VK1aQkpJCRkYG/fr1c+aVNV0JHnXr1sXTs8kYw/Hjx93Svv7664DCkmZmZnoNU1rZMMasFZGkQOvxK+azolQVVClUDlq3bu13eqBKYfjw4VaJXWUIS6skRQkWqhQqB5MmTWL8+PHk5eU506Kiopg0aVKJsmZtke3ExeppQP2LoQzpijthqRjUXFUpD/5MQ4XUxFTxmxEjRgAwbtw48vPzadOmDZMmTXKml6C8JrtBMp2taKw2V9U1BiUssGKNwR/y8/ODWr9iLZdffjkAS5Ys8VqmzvQvA1YK0rNqzKrrGoOiKApU65FCsAjLqSSl+hHskYJShSnt4Z96iS099VHvyqLnJRUmbmVARwxKpSE2NraEAsnMzCxTulIF8WdEcCTX9whCcUMVg1JpKI8PnOLpShXGl1LYtsn3yEJxIyynktQqSfGEFfbq4WJlVPvuuwO6Pv/FFy2SpIrga01h2yabQ78qvNZgtVVSWCqGBg0akJ6eHmoxFD+pKIsiB9XZi6bigdIWmivYy2socLxEv/baa4esqC8sFYNSNYmNjbVkOiiQdCvMVQN921cspjTro/RJntOriFIIBkFbYzDGvGGM2WeM+b5Y+l3GmB+NMZuMMU8Hq30l/Ai1UlCqJovnZHBi9IWc6HqKE11PsfjAUmL+foMzvW+9IvrWKyqR7lpecSeYI4bpwEvAW44EY8wAYCiQICL5xpgmQWxfCTOseMhnZGTQp08f55u/q9sKf9KrClaMWqrKOkVxdyXlCaKkuBM0xSAinxtj2hZLvgN4SkTy7WX2Bat9JXwJRCkE4uNoxYoV+hCoZPja8awEj4peY+gI9DXGpAEngAdF5NsKlkEJIYFMB/Xp08eZXl4vqTnXXBPU+1OUqkBF72OIBBoB5wMPAe8aY0zxQjk5OSQlJTkPtVCqGli1RhCI62ylejF79my++eYbVq5cSceOHZk9e3aoRbKc9PR057MSiLGizooeMewC3rdHGvrGGFOE7UZyXAvFxsaiTvSqHgOGDS+XS2RH+omuFsRTmDev4m5YCSmzZ89m/PjxzvWlHTt2MH78eADvXlorIampqaSmpgJgjNlvRZ1B9a5qX2NYKCJd7Z9vB1qIyKPGmI7AMqCNFBNCvatWLvzdx/Dpp5+GPA6CmpqepqosPnujY8eO7Nixo0R6mzZt2LJli1ta7dq1K0qsoBL23lWNMe8AXwOdjDG7jDFjgDeAdnYT1tnAqOJKQalcZGRksGrVKlasWEHbtm19TtcEunCsKGVh586dZUpXThNMq6QbvWSNDFabSsWSkZFBamqqc6i+fft255A2JSXF63XlnQ4KF3cWSuWgdevWHkcM3sKGKqdRJ3pKuZk4caJb6EWAvLw8Jk6c6PUaXThWKopJkyYRFRXlluYtPKjiTli6xFAnepUDT29jvtIDXjhWLKWqb5Irc3jQSoyG9lTChrZt27J9+/YS6XFxcWRnZ5dID3T3sS4chx/hrBgc+BMeVBef3dGpJKXcpKWleRyqp6WlhUgiRVGsQBWDUm5SUlJIT093vm3FxcWRnp7uc+FZUZTwJyzXGJTKQ0pKCq+99hqgEdIUpaqgIwZFURQ7ZY0hXlVji6tiUBRFQeN9uBKWisFhrmo3v1IURQkqlV0pLFiwwLG5VM1VlfDAX19Jaq6qeCLYJq/+mKu2atWq0ioFV6wyV9XFZ6XC0Ae7Egr8CfZjRRjZcFMSgRCWU0mKoigViRVhZ6sSqhgURVHsBLLWUJVQxaAETGZmZpUz11OqF2ZtEWbqZwwYNpz9/7QFhzJri8qU7qAqmLyGpWJQqyRFUSoUl4iBJPUvd3qorJvUKkmpvIwbF2oJlDAkHBzx1WnWKmClsPzI5yE3eVUneoqiKFYRoFJgTeXeB1GckCgGY8x9xphNxpjvjTHvGGPqhEIOpWxUhblTRfFIgEqBh2+wxOQ1XKhwxWCMaQncDSSJSFcgAqh6kTOqGJV9Z6ii+EUAaw1VyeQ1VFNJkUBdY0wkEAXsCZEcip+oUlCqPGGwAB0uVLhiEJHdwLPADmAvcEhEPnUtk5OTQ1JSkvNIT0+vaDGrLOWdDsrIyKBPnz7k5+eTn5/Pp59+yvDhw8uUrihhTVJ/WPq7+8O/DOlWmryWhfT0dOezEogpVyXFqHCrJGPMmcA84AYgF3gPmCsisxxl1CopOATyJtOnTx9nenljN+dcc01Q70+pnISFVdL3NQOvxIIRh/QM7F29MvtKugz4VURyAIwx7wMXALN8XqUERMBrBHZT08w9e0hZupT3LruM/vPmwbx5fqcrSrhyouupgOuoc1ng01DhQihGDL2BN4DzgOPAdGCNiPyfo4yOGKyltId/y5YtadiwoXMayWN53YOgBIlwGDFYQZ3pX3p/+KdPsqWlf+ZTWci4SwKSodLuYxCR1cBcYB2w0S6DLiIEkdLmNtdHNGTFEd9zpIqi+ObE6As50fUUJ7qeYvGBpcT8/QYWz8ngxOgL6VuviL71ikqkFy8fLoTE7baIPAY8Foq2qyOL52TQr9+FgG24vGLFClL+nkKGPf3yDNuD/+8Hlrqlu5ZXlGARqDv2cBtxeFtrO3ToUKlrc+GC7nyuBvizQFzal1ZRlNLx9fvavHmz3wYboSYsFYM60fOf/v37+71XwNOXcPbs2axatYoNGzZQo0YN9u7d67G8oii+8faQf+yxx9iwYQMFBQXcdtttzJ4922f58qBO9KoZViwc5+fne1UK48aNcwu5GRUVxcsvv0zz5s3dymv0NSVcCbepJFdmz57N+PHjycvLc6Y5fmMjRpR0+FC7du2A2rNq8VkVQ5gTGxvr05T08ccfd6Z5UyLeYi137NiRHTt2lEhv06YNW7ZscUtTxaCEK+GsGMryG4PwUQwa8znM8aUUdu/ezapVq8jPz6dZs2YcO3aMBQsWlCjvujnNlZ07d5YpXVGUslFZf2NhucagnMaXUkhNTXWOBn7//XdOnTrF7t27S5T3RuvWrcuUrihK2aisvzGdSgpzHA/+4msEpQ1RXctfYd+FXJyMrVtJXbmSvIICZ1pUZCTpffuS0qFDcG5IUcKQYE1HVdY1hrAcMahVkjueFo59DVH9tXZI6dCB9L59qV3D9jWIi45WpaAoFjJixAhefvll5wO/TZs2XpWCg/I4ujzjjDMYPHgwqFVS9eDTTz/1+JBv06YN+/btK1G+SZMmFBUVuZUvbeG4v10BZyYnWyi5olQegr2AffnllwOwZMkSn+W+/vrrgHya6eJzFcDxD/YVucnbprO8vDxq167tZnFUu3Zt8vLymDdvXlhtllGUyoy/D3UrCJe4J2E5laScZteuXSUe8v369WP//v1MnTrVbYg6depU9u/fr0pBUSop4aAUQEcMISMjI8Npatq2bVvS0tI8up7wNQ00CnizUSMAMgcNgq++sh1lRKeQFMUzs2fP5ptvviE/P5+OHTsyadIkn+sD3vB3tBEuMaNVMYSAjIwMN1PT7du3O7azq18iRQkTHBZFjt/pjh07GD9+PEC5lIM/eLJCdERC9CfdKnQqKQRMnDjRzXwNIC8vj4kTJ4ZIIkVRivPoo496/J0++uijQW23vBESrfRpFpZWSR06dJABAwaQnJxMchhOc2RkZDBmzBjy8/OJi4vzOg3kjRo1auCp340xFBUVi32gAXLCD/voLiACjWMeDjJUcWqkp+Pp6WiAIg/9b4VlU3mVwl133cX27dt54403fhaRgO3Nw1IxhLO5qmMaqPiGlfT0dM/KweXBnrlnD8OXLiXCGH4/frxE0aZ16/LbzTe7le/fooW1N1DdseKBqpymCiuXtm+/zfajR0ukx0VHk33TTSXSrVAMrVq1KtdIwZFep04ddaIXCtq2bcv27dtLpMfFxZGdnV3yAg9v/LrjOISoYgg/wlS5lPV3Gg7O/KxSDCFZfDbGDAJeACKAaSLylGt+Tk6O12szMjKYOHEiO3bsoE2bNmWexilLHZ5W/z25oXCk+2tC5vhSjVmxgvyiIuKio0k777ygKYX0H34gtXPnoNRdJkp5KKe//z6p11xTQcKUn3CQ87uk0n/784BrgyzHuRa8wIXN97MYFf079cXs2bN59NFH2blzJ61bt/ZlHRVjRXsVPmIwxkQAW4DLgV3At8CNIrLZUaZevXpy7NixEteWZRpnwYIFHtcnylTHoEEkx8W5pZV1eLlg+/YSdViZ70+ZpPffZ00pD7JS5TjnHJIvvth7/uef+8z3p0zSzTezZubMoLbxfFISvnZ5rACf+QApgK9lPn/q+Nffr6VDL+8PmK3fbA0oH+DN+6dzy+TRQW3Dijo+/euHAf3fF0ycGPBvxIrf0PsDB3L11Vd7zf/oo4985nsrUxZ/S3Xq1MkTkXo+G/GDUIwYegE/i8g2AGPMbGAosNnnVfi25inxUJ8wgeSFC0vW8fbbnuu4/XZSPv/cvQ4PX5a0887zOLxMO+88jzKHg2Lwh9LqeHTRDL5uX9IFh4NPFn3iM9+fMrtP/cHf9swNahufXJHAvjuv9J7/0ic+8wH+uH867/l44PpTx88vfeLzgfnzNz8HlO8PVrRhRR2B/t/3R0WRXMpodEFams8ypeWzcmWpI95FI0dyzeLF3vM//9xnPsBHBQUlFIMv66hgmc2GYsRwHTBIRMbaP98M9BaRO13KnAAKXS7LAfYDPX1UvbbY5wbAIQ/lrKijEdASqAWcBHYDB73U6a0Oq/L9KRODrf8CqSMc5LSiDSvuo7rIWRH3AZVDzor4DXkrU9ozKwaItX+OEJE6pbRRKmG5wc2KG1MURVHKRyg2uO0GXKNUtLKnKYqiKGFAKBTDt0AHY8xZxphawAhgfgjkUBRFUTxQ4YpBRAqAO4EvgaNAN6Cut/LGmEHGmJ+MMT8bYx52ST/LGLPanj7HrmQsxxjTyBizxBiz1f73TA9lBhhjslyOE8aYP9nzphtjfnXJSwyVnPZyhS6yzHdJD6f+TDTGfG2M2WSM2WCMucElL2j96e275pJf2943P9v7qq1L3iP29J+MMQOtkqmcct5vjNls77tlxpg4lzyP//8QyTnaGJPjIs9Yl7xR9u/IVmPMqBDLOcVFxi3GmFyXvArpT2PMG8aYfcaY773kG2PMi/Z72GCM6eGSV/a+FJGQHEBnoBOQCSR5KRMB/AK0w7bQux7oYs97FxhhP38VuCNIcj4NPGw/fxj4TynlG2FbiI6yf54OXFcB/emXnMBRL+lh059AR6CD/bwFsBdoGMz+9PVdcykzHnjVfj4CmGM/72IvXxs4y15PRJD6zx85B7h8/+5wyOnr/x8iOUcDL3m4thGwzf73TPv5maGSs1j5u4A3QtCfFwM9gO+95F8FfILNY8f5wOpA+jJkTvRE5AcR+amUYk7TVhE5CcwGhhpjDHAJ4LBxmwH8KUiiDrXX72871wGfiEheKeWspqxyOgm3/hSRLSKy1X6+B9jHaauLYOHxu1asjKvsc4FL7X03FJgtIvki8ivws72+kMgpIstdvn+rsK3jVTT+9Kc3BgJLROSgiPwBLAEGhYmcNwLvBEkWr4jI53i3fASbzG+JjVVAQ2NMc8rZl+HuXbUl4BrceJc9rTGQK7ZpKdf0YNBURPbaz38DmpZSfgQlvzhp9uHdFGNMYNG+veOvnHWMMWuMMasc012EcX8aY3phe5P7xSU5GP3p7bvmsYy9rw5h6zt/rrWKsrY1BtubpANP//9g4K+c19r/l3ONMQ6jlLDsT/uU3FnAZy7JFdWfpeHtPsrVl0E1VzXGLAWaeciaKCIfBrPtsuBLTtcPIiLGGK8bP+wauhvguovlEWwPwFpAOvBXYFII5YwTkd3GmHbAZ8aYjZRuWx0KOR39ORMYJSIOt7OW9WdVxxgzEkjCfSN2if+/iPziuYagswB4R0TyjTHjsI3GLgmRLP4wApgrIq57rMKpPy0jqIpBRC4LsApvpq0HsA2VIu1vbgGZvPqS0xjzuzGmuYjstT+ofG29vR74QEROudTteDvON8a8CTwYSjlFZLf97zZjTCZwLja3OmHVn8aYM4CPsL1ErHKp27L+LIY/ZtSOMruMMZHYNiMd8PNaq/CrLWPMZdgUcT8RcQYG9/L/D8aDrFQ5ReSAy8dp2NafHNf2L3ZtpuUSnm7L3//dCOAvrgkV2J+l4e0+ytWX4T6V5NG0VWyrKsuxzeeDLcplsEYg8+31+9NOiflH+8PPMY//J8CjVYEFlCqnMeZMx9SLMSYGuBDYHG79af9ff4BtznRusbxg9ac/ZtSusl8HfGbvu/nACGOzWjoL6AB8Y5FcZZbTGHMuMBUYIiL7XNI9/v9DKGdzl49DgB/s54uBK+zynglcgfsovELltMt6DrbF269d0iqyP0tjPvBnu3XS+cAh+0tU+fqyIlbUvayiD8M235UP/A4stqe3AD52KXcVNqd7v2B7e3Skt8P24/sZeA+oHSQ5GwPLgK3AUqCRPT0Jm2dYR7m22LRzjWLXfwZsxPYAmwVEh0pO4AK7LOvtf8eEY38CI4FTQJbLkRjs/vT0XcM2TTXEfl7H3jc/2/uqncu1E+3X/QRcGeTfTmlyLrX/phx9N7+0/3+I5Pw3sMkuz3LgHJdrb7X388/ALaGU0/75ceCpYtdVWH9ie+Hca/9d7MK2dnQ7cLs93wD/td/DRlwsPcvTl2EZj0FRFEUJHeE+laQoiqJUMKoYFEVRFDdUMSiKoihuqGJQFEVR3FDFoCiKorihikGplhhj2hhjjhpjWvhZvr8xpqD0kopS+VHFoAQFY0ymMSbf/vA9amzugO91yc82NvfkR4wxh4wxPxpjphpjOtjz27hce9QYU2CMOenyeZMfMjQxxswwxhwwxhw2NtfILQBEZIeIRIvNSR/GmKuMMZ8ZY/YbY/4wxqw0xvQNUveUCWNMW2OMGGMsdYZnbG6vf7ayTqVqoIpBCSZP2h++0dg2raUZYy53yR8rIvWBhti8QxogyxhzvsuD23F9JvAvl7R4Xw0bY+pg20h3Ept794ZACrYYIJ44E/g/oD02T65vA5+Y047dFKXaoIpBqRDE5u9oMzYng8XzRER+EpFUbC4HnrOgyVHYlMF4EdkvIkUisklEDkPJt3ARyRCRD0QkV0QKROQVbErkPNdKjS3oyXZjzEFjCxoU7Y8wxpg7jC0YzCFj88TZ1yXvcWNzPOhaPtMY83f7x/X2vz/ZR0v/sJcRY8y99pHQEWPMcmNMey914HLNRcaYPtjibrRzGYX19+delKqPKgYl6Nj9t1wInIOLrxkvzAHON8ZEBdjsAGxuN6bbp5J+NMbc5+/FxphuQAw29wIOIoBkoDu2QFMdgcl+1HUj8CTwZ2wuQV4DFhmXyGqlkGD/28k+WnrSJS8Vm9+mJtjcS8w3xkSUVqGIfI3NpcI2l1FYpp/yKFUcVQxKMJlobGEQjwFfABmU7lxuF7bvpcfQpGUgBpty+AZojm0qa6IxJqW0C40xTbB5nH1W7AGDXPiriBwSkd+BR7E5Livtd3QLMFVEVttHI68DG4CbynZLHnlORH4WkePABOBsoLcF9SrVGFUMSjBJE5GGIhKFzSVwF+CNUq5pBRQBfwTY9hFgt4i8ICInRWQNNqd7PqOI2RenlwOfYov9UJztLufZ2MJ5xpQiS2vg12Jpv+DuJrm8ZDtOxBa1LYfQRGxTqhCqGJQKQUR2YYsrfU0pRW/AFq820NCoWYAnD5G+AgO1BVZiC816p3j2MOk6/dMWm3fg/aXIstNe1pV2nI6sdQSoVyzf1Yy2CO8467VPv8ViG3WVqNeDaa6vepVqjCoGpUIwxjQDhnN6IbV4fgdjzCvYfNpbEXxnOtDYGPMXY0yEMSYBm1XS+17aPwfbdNc7IuKr/X8bY86wTzc9DsyU09HlfMkyzhjTyxgTaYy5BUjEZvkEsBboYYzpac+/E1sISQc52B7iHTzUfZ8x5my7FdZT2IK9r3ap90/GmFhjTH0grdi1vwFNjC0okqI4UcWgBJN/OCxesCmE33GfV59mt6Y5jC1aWyRwroh8FWjDIrIdm5/9scBhYC7wuIjM8XLJX7HFwr232P4J1zWJQrucG7HFXdgG3O+HLG8DT2CbyjoA3AFcZZcR+6LvZGARNp/7TYEvXa4/DvwDeMcYk2uMcQ2ROg2bssvBtkg9VE6HnpyCLfjNL9hGUB8VE205tuDwv9rr7YeigMZjUKonxpizsQUuaSouUc4qE8YWL7uviHwRalmUqoWOGJTqSg/gELY3bUVRXFDFoFRKjDF9i035uB5/K+XaycALwF1eFpgrTBZFCUd0KklRFEVxQ0cMiqIoihuqGBRFURQ3VDEoiqIobqhiUBRFUdxQxaAoiqK4oYpBURRFceP/AUHvmfwEVN9wAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data_from_hist(data=data_6j2b_SR_hists, MC_errors=data_6j2b_SR_errors, samples_to_plot=samples_SR, \n", " x_variable='BDT_6j2b_output', region_label='2L-Z-6j2b')" ] }, { "attachments": { "6j2b_SR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 10(c) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![6j2b_SR.png](attachment:6j2b_SR.png)\n", "\n", "FIG. 10(c). The BDT distribution for the OS dilepton signal regions, (c) 2l-Z-6j2b. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons. The shaded band represents the total uncertainty. The last bin of each distribution contains the overflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 5j2b Signal Region plot with data-driven $t\\bar{t}$\n", "\n", "Use the data_driven() function defined [above](#data_driven) to get the histograms of Data and MC in the 5j2b Signal Region." ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [], "source": [ "data_5j2b_SR_hists, data_5j2b_SR_errors = data_driven(data_SR=data_5j2b_SR, data_CR=data_5j2b_CR, \n", " x_variable='BDT_5j2b_output')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data_from_hist() function defined [above](#plot_data_from_hist) to compare Data with MC in BDT outputs in the 5j2b Signal Region. " ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data_from_hist(data=data_5j2b_SR_hists, MC_errors=data_5j2b_SR_errors, samples_to_plot=samples_SR, \n", " x_variable='BDT_5j2b_output', region_label='2L-Z-5j2b')" ] }, { "attachments": { "5j2b_SR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 10(b) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![5j2b_SR.png](attachment:5j2b_SR.png)\n", "\n", "FIG. 10(b). The BDT distribution for the OS dilepton signal regions, (b) 2l-Z-5j2b. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons. The shaded band represents the total uncertainty. The last bin of each distribution contains the overflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## 6j1b Signal Region plot with data-driven $t\\bar{t}$\n", "\n", "Use the data_driven() function defined [above](#data_driven) to get the histograms of Data and MC in the 6j1b Signal Region." ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [], "source": [ "data_6j1b_SR_hists, data_6j1b_SR_errors = data_driven(data_SR=data_6j1b_SR, data_CR=data_6j1b_CR, \n", " x_variable='BDT_6j1b_output')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the plot_data_from_hist() function defined [above](#plot_data_from_hist) to compare Data with MC in BDT outputs in the 6j1b Signal Region. " ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data_from_hist(data=data_6j1b_SR_hists, MC_errors=data_6j1b_SR_errors, samples_to_plot=samples_SR, \n", " x_variable='BDT_6j1b_output', region_label='2L-Z-6j1b')" ] }, { "attachments": { "6j1b_SR.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "Compare this with Figure 10(a) of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009).\n", "\n", "![6j1b_SR.png](attachment:6j1b_SR.png)\n", "\n", "FIG. 10(a). The BDT distribution for the OS dilepton signal regions, (a) 2l-Z-6j1b. The “Other” background contains SM processes with small cross sections producing two opposite-sign prompt\n", "leptons. The shaded band represents the total uncertainty. The last bin of each distribution contains the overflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## BDT feature importances\n", "\n", "First, we order the input variables by feature importances." ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Nmbjj_top': 0.14880980781153252,\n", " 'pt6_jet': 0.13809996152015147,\n", " 'NJetPairsZMass': 0.12217987858169214,\n", " 'MbbPtOrd': 0.09273903446533227,\n", " 'HT_jet6': 0.07963488156813663,\n", " 'dRbb': 0.07541096503861816,\n", " 'dRll': 0.07373060862820331,\n", " 'pt4_jet': 0.07193439962375803,\n", " 'eta_ll': 0.07158224055223357,\n", " 'dRjjave_jet': 0.05012711086834696,\n", " 'MWavg': 0.02903602288338741,\n", " 'pT1b': 0.012929368519663878,\n", " 'MaxMMindRlepb': 0.009668519205504737,\n", " 'H1': 0.007859470862618158,\n", " 'ptll': 0.007630042587966935,\n", " 'CentrJet': 0.0043949653384636475,\n", " 'MjjMindR': 0.00423272194439008}" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_importance_dict_6j2b = {} # dictionary to hold features and their importance\n", "for i in range(len(BDT_6j2b_inputs)): # loop over BDt input variables\n", " feature_importance_dict_6j2b[BDT_6j2b_inputs[i]] = bdt_6j2b.feature_importances_[i] # get feature importance from bdt\n", " \n", "# sort the feature importance dict by feature importance\n", "sorted_feature_importance_dict_6j2b = { k:v for k,v in reversed(sorted(feature_importance_dict_6j2b.items(), \n", " key=lambda item: item[1])) }\n", "sorted_feature_importance_dict_6j2b\n" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'pt4_jet': 0.17079617647815348,\n", " 'NJetPairsZMass': 0.13557255844220467,\n", " 'dRjjave_jet': 0.1329411996184684,\n", " 'dRll': 0.1315712131934299,\n", " 'MbbPtOrd': 0.11444977309578826,\n", " 'HT_jet6': 0.11048999187674598,\n", " 'dRbb': 0.06981725065712244,\n", " 'pt5_jet': 0.04633946223945213,\n", " 'eta_ll': 0.026117860433350907,\n", " 'MjjMindR': 0.02558700231375926,\n", " 'MuuPtOrd': 0.012501984849613988,\n", " 'H1': 0.009858093509725723,\n", " 'CentrJet': 0.00931277370926509,\n", " 'ptll': 0.004644659582919478}" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_importance_dict_5j2b = {} # dictionary to hold features and their importance\n", "for i in range(len(BDT_5j2b_inputs)): # loop over BDt input variables\n", " feature_importance_dict_5j2b[BDT_5j2b_inputs[i]] = bdt_5j2b.feature_importances_[i] # get feature importance from bdt\n", " \n", "# sort the feature importance dict by feature importance\n", "sorted_feature_importance_dict_5j2b = { k:v for k,v in reversed(sorted(feature_importance_dict_5j2b.items(), \n", " key=lambda item: item[1])) }\n", "sorted_feature_importance_dict_5j2b\n" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'NJetPairsZMass': 0.2367403979567505,\n", " 'pt6_jet': 0.16679658398299607,\n", " 'eta_ll': 0.1486361221909552,\n", " 'HT_jet6': 0.13780214925444875,\n", " 'pt4_jet': 0.07823838661289743,\n", " 'dRll': 0.06680534445560618,\n", " 'MWavg': 0.033925199141243936,\n", " 'pT1b': 0.033176605005005626,\n", " 'MuuPtOrd': 0.027811933121933148,\n", " 'MaxMMindRlepb': 0.027161853614707295,\n", " 'dRjjave_jet': 0.014551930534264046,\n", " 'H1': 0.01397039832195171,\n", " 'MjjMindR': 0.008369768989960611,\n", " 'CentrJet': 0.005552178268270512,\n", " 'ptll': 0.00046114854900901575}" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_importance_dict_6j1b = {} # dictionary to hold features and their importance\n", "for i in range(len(BDT_6j1b_inputs)): # loop over BDt input variables\n", " feature_importance_dict_6j1b[BDT_6j1b_inputs[i]] = bdt_6j1b.feature_importances_[i] # get feature importance from bdt\n", " \n", "# sort the feature importance dict by feature importance\n", "sorted_feature_importance_dict_6j1b = { k:v for k,v in reversed(sorted(feature_importance_dict_6j1b.items(), \n", " key=lambda item: item[1])) }\n", "sorted_feature_importance_dict_6j1b\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we write a description of each BDT variable in words." ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [], "source": [ "feature_descriptions = {'ptll':'$p_T$ of the lepton pair',\n", " 'pt4_jet':'$p_T$ of the 4th jet',\n", " 'pt5_jet':'$p_T$ of the 5th jet',\n", " 'pt6_jet':'$p_T$ of the 6th jet',\n", " 'dRll':'$\\Delta R_{\\eta}$ between the two leptons',\n", " 'NJetPairsZMass':'Number of jet pairs with mass within a window of 30 GeV around 85 GeV',\n", " 'Nmbjj_top':'Number of top-quark candidates',\n", " 'MjjMindR':'Invariant mass of the two jets with the smallest $\\Delta R_{\\eta}$',\n", " 'MuuPtOrd':'Invariant mass of the two untagged jets with the highest $p_T$',\n", " 'MbbPtOrd':'Invariant mass of the two jets with the highest value of the b-tagging discriminant',\n", " 'CentrJet':'Scalar sum of $p_T$ divided by the sum of energy of all jets',\n", " 'dRjjave_jet':'Average $\\Delta R_{\\eta}$ of all jet pairs',\n", " 'MaxMMindRlepb':'Maximum invariant mass of a lepton and the b-tagged jet with the smallest $\\Delta R_{\\eta}$',\n", " 'H1':'First Fox–Wolfram moment built from jets and leptons',\n", " 'HT_jet6':'Sum of jet $p_T$, using up to six jets',\n", " 'eta_ll':'$\\eta$ of dilepton system',\n", " 'MWavg':'Sum of the two closest two-jet invariant masses from jjj1 and jjj2 divided by two',\n", " 'dRbb':'$\\Delta R_{\\eta}$ between two jets with the highest value of the b-tagging discriminant in the event',\n", " 'pT1b':'$p_T$ of the b-tagged jet with the highest $p_T$'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we print the BDT input variables ranked by feature importances\n", "\n", "If there are any features not used in the BDT for a particular region, print a dash." ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| $p_T$ of the lepton pair |15|14|15|\n", "| $p_T$ of the 4th jet |5|1|8|\n", "| $p_T$ of the 5th jet |-|8|-|\n", "| $p_T$ of the 6th jet |2|-|2|\n", "| $\\Delta R_{\\eta}$ between the two leptons |6|4|7|\n", "| Number of jet pairs with mass within a window of 30 GeV around 85 GeV |1|2|3|\n", "| Number of top-quark candidates |-|-|1|\n", "| Invariant mass of the two jets with the smallest $\\Delta R_{\\eta}$ |13|10|17|\n", "| Invariant mass of the two untagged jets with the highest $p_T$ |9|11|-|\n", "| Invariant mass of the two jets with the highest value of the b-tagging discriminant |-|5|4|\n", "| Scalar sum of $p_T$ divided by the sum of energy of all jets |14|13|16|\n", "| Average $\\Delta R_{\\eta}$ of all jet pairs |11|3|10|\n", "| Maximum invariant mass of a lepton and the b-tagged jet with the smallest $\\Delta R_{\\eta}$ |10|-|13|\n", "| First Fox–Wolfram moment built from jets and leptons |12|12|14|\n", "| Sum of jet $p_T$, using up to six jets |4|6|5|\n", "| $\\eta$ of dilepton system |3|9|9|\n", "| Sum of the two closest two-jet invariant masses from jjj1 and jjj2 divided by two |7|-|11|\n", "| $\\Delta R_{\\eta}$ between two jets with the highest value of the b-tagging discriminant in the event |-|7|6|\n", "| $p_T$ of the b-tagged jet with the highest $p_T$ |8|-|12|\n" ] } ], "source": [ "sorted_feature_importance_keys_6j1b = list(sorted_feature_importance_dict_6j1b.keys())\n", "sorted_feature_importance_keys_5j2b = list(sorted_feature_importance_dict_5j2b.keys())\n", "sorted_feature_importance_keys_6j2b = list(sorted_feature_importance_dict_6j2b.keys())\n", "for k,v in feature_descriptions.items():\n", " print('|',v,'|', end = '')\n", " if k in sorted_feature_importance_keys_6j1b: print(sorted_feature_importance_keys_6j1b.index(k)+1, end = '')\n", " else: print('-', end = '')\n", " print('|', end = '')\n", " if k in sorted_feature_importance_keys_5j2b: print(sorted_feature_importance_keys_5j2b.index(k)+1, end = '')\n", " else: print('-', end = '')\n", " print('|', end = '')\n", " if k in sorted_feature_importance_keys_6j2b: print(sorted_feature_importance_keys_6j2b.index(k)+1, end = '')\n", " else: print('-', end = '')\n", " print('|')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The details of the variables used are given in the Table below.\n", "\n", "| Definition | 6j1b | 5j2b | 6j2b |\n", "|------|------|------|------|\n", "| $p_T$ of the lepton pair |15|14|15|\n", "| $p_T$ of the 4th jet |5|1|8|\n", "| $p_T$ of the 5th jet |-|8|-|\n", "| $p_T$ of the 6th jet |2|-|2|\n", "| $\\Delta R_{\\eta}$ between the two leptons |6|4|7|\n", "| Number of jet pairs with mass within a window of 30 GeV around 85 GeV |1|2|3|\n", "| Number of top-quark candidates |-|-|1|\n", "| Invariant mass of the two jets with the smallest $\\Delta R_{\\eta}$ |13|10|17|\n", "| Invariant mass of the two untagged jets with the highest $p_T$ |9|11|-|\n", "| Invariant mass of the two jets with the highest value of the b-tagging discriminant |-|5|4|\n", "| Scalar sum of $p_T$ divided by the sum of energy of all jets |14|13|16|\n", "| Average $\\Delta R_{\\eta}$ of all jet pairs |11|3|10|\n", "| Maximum invariant mass of a lepton and the b-tagged jet with the smallest $\\Delta R_{\\eta}$ |10|-|13|\n", "| First Fox–Wolfram moment built from jets and leptons |12|12|14|\n", "| Sum of jet $p_T$, using up to six jets |4|6|5|\n", "| $\\eta$ of dilepton system |3|9|9|\n", "| Sum of the two closest two-jet invariant masses from jjj1 and jjj2 divided by two |7|-|11|\n", "| $\\Delta R_{\\eta}$ between two jets with the highest value of the b-tagging discriminant in the event |-|7|6|\n", "| $p_T$ of the b-tagged jet with the highest $p_T$ |8|-|12|\n", "\n", "Table: The definitions and ranking of input variables for the BDT in the OS dilepton analysis. Jets and leptons are ordered in descending order of $p_T$. Only the first eight jets are considered when calculating the input variables.\n", "\n", "The variables with the largest discriminative power are found to be:\n", "* $p_T$ of the 6th jet\n", "* $p_T$ of the 4th jet\n", "* Number of jet pairs with mass within a window of 30 GeV around 85 GeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This can be compared to Table 11 of the ATLAS published paper [Measurement of the $t\\bar{t}Z$ and $t\\bar{t}W$ cross sections in proton-proton collisions at $\\sqrt{s}$ = 13 TeV with the ATLAS detector](https://journals.aps.org/prd/pdf/10.1103/PhysRevD.99.072009)\n", "\n", "| Definition | 6j1b | 5j2b | 6j2b |\n", "|------|------|------|------|\n", "| $p_T$ of the lepton pair |8|11|8|\n", "| $p_T$ of the 4th jet |6|12|6|\n", "| $p_T$ of the 5th jet |-|14|-|\n", "| $p_T$ of the 6th jet |9|-|11|\n", "| $\\Delta R_{\\eta}$ between the two leptons |7|8|12|\n", "| Number of jet pairs with mass within a window of 30 GeV around 85 GeV |4|6|4|\n", "| Number of top-quark candidates |-|-|17|\n", "| Invariant mass of the two jets with the smallest $\\Delta R_{\\eta}$ |13|7|14|\n", "| Invariant mass of the two untagged jets with the highest $p_T$ |15|13|-|\n", "| Invariant mass of the two jets with the highest value of the b-tagging discriminant |-|10|9|\n", "| Scalar sum of $p_T$ divided by the sum of energy of all jets |2|1|2|\n", "| Average $\\Delta R_{\\eta}$ of all jet pairs |5|4|5|\n", "| Maximum invariant mass of a lepton and the b-tagged jet with the smallest $\\Delta R_{\\eta}$ |14|-|13|\n", "| First Fox–Wolfram moment built from jets and leptons |3|2|1|\n", "| Sum of jet $p_T$, using up to six jets |12|5|10|\n", "| $\\eta$ of dilepton system |1|3|3|\n", "| Sum of the two closest two-jet invariant masses from jjj1 and jjj2 divided by two |10|-|15|\n", "| $\\Delta R_{\\eta}$ between two jets with the highest value of the b-tagging discriminant in the event |-|9|7|\n", "| $p_T$ of the b-tagged jet with the highest $p_T$ |11|-|16|\n", "\n", "Table 11: The definitions and ranking of input variables for the BDT in the OS dilepton analysis. Jets and leptons are ordered in descending order of $p_T$. Only the first eight jets are considered when calculating the input variables.\n", "\n", "The variables with the largest discriminative power are found to be:\n", "* $\\eta$ of dilepton system\n", "* Scalar sum of $p_T$ divided by the sum of energy of all jets\n", "* First Fox–Wolfram moment built from jets and leptons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Putting everything into a BDT means we only have 1 variable to optimise. The signal and background distributions are separated much better when looking at BDT output, compared to individual variables. Cutting on BDT output also achieves much higher S/B values than on individual variables.\n", "\n", "BDTs can achieve better S/B ratios because they find correlations in many dimensions that will give better signal/background classification.\n", "\n", "Hopefully you've enjoyed this discussion on optimising for signal to background ratio, and in particular how a BDT can be used to facilitate this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Going further\n", "\n", "If you want to go further, there are a number of things you could try: \n", "* **Plot BDT output in 5j2b Signal Region**. Complete the code cells in '[Training Decision Trees (5j2b)](#BDT_5j2b_training)' and '[5j2b Signal Region plot](#plot_5j2b_SR)' to get a plot of BDT output distribution in the 5j2b Signal Region. Run these cells to see the plots. You may find the code cells in '[Training Decision Trees (6j2b)](#BDT_6j2b_training)' and '[6j2b Signal Region plot](#plot_6j2b_SR)' helpful.\n", "* **Plot BDT output in 6j1b Signal Region**. Complete the code cells in '[Training Decision Trees (6j1b)](#BDT_5j2b_training)' and '[6j1b Signal Region plot](#plot_6j1b_SR)' to get a plot of BDT output distribution in the 6j1b Signal Region. Run these cells to see the plots. You may find the code cells in '[Training Decision Trees (6j2b)](#BDT_6j2b_training)' and '[6j2b Signal Region plot](#plot_6j2b_SR)' helpful.\n", "* **Plot BDT output in 5j2b Control Region**. Complete the code cells in '[5j2b Control Region plot](#plot_5j2b_CR)' to get a plot of BDT output distribution in the 5j2b Control Region. Run these cells to see the plots. You may find the code cells in '[6j2b Control Region plot](#plot_6j2b_CR)' helpful.\n", "* **Plot BDT output in 6j1b Control Region**. Complete the code cells in '[6j1b Control Region plot](#plot_6j1b_CR)' to get a plot of BDT output distribution in the 6j1b Control Region. Run these cells to see the plots. You may find the code cells in '[6j2b Control Region plot](#plot_6j2b_CR)' helpful.\n", "* **Plot BDT output in 5j2b Signal Region with data-driven $t\\bar{t}$**. Complete the code cells in '[5j2b Signal Region plot with data-driven $t\\bar{t}$](#5j2b_data_driven)' to get a plot of BDT output distribution in the 5j2b Signal Region. Run these cells to see the plots. You may find the code cells in '[6j2b Signal Region plot with data-driven $t\\bar{t}$](#6j2b_data_driven)' helpful.\n", "* **Plot BDT output in 6j1b Signal Region with data-driven $t\\bar{t}$**. Complete the code cells in '[6j1b Signal Region plot with data-driven $t\\bar{t}$](#6j1b_data_driven)' to get a plot of BDT output distribution in the 6j1b Signal Region. Run these cells to see the plots. You may find the code cells in '[6j2b Signal Region plot with data-driven $t\\bar{t}$](#6j2b_data_driven)' helpful.\n", "* **Add variables into the BDTs**. Start by adding them in the list of '[BDT_6j2b_inputs](#BDT_6j2b)' and/or '[BDT_5j2b_inputs](#BDT_5j2b)' and/or '[BDT_6j1b_inputs](#BDT_6j1b)'. Then uncomment the commented variables in '[Can we process the data yet?!](#load_data)' (lines 116-125). Cell -> Run All Below. See how things look with all variables added.\n", "* **Add stricter requirements on good leptons**. Uncomment the lines in '[Can we process the data yet?!](#load_data)' regarding *lep_z0* (lines 14, 61, 148). A Ctrl-f search for *lep_z0* might help. Uncomment the lines in '[the select_leptons function](#select_leptons)' regarding *lep_z0* (longitudinal impact parameter) (lines 4 and 32-35). Cell -> Run All Below. See how things look with this selection added.\n", "* **Add stricter requirements on good jets**. Uncomment line 34 in '[Can we process the data yet?!](#load_data)'. Uncomment the line for *jet_eta* in '[the select_jets function](#good_jets)' (line 2). Change the selection for jets with $p_T$ below 60 GeV to include a selection on jet_eta in '[the select_jets function](#good_jets)' (line 8/9). Cell -> Run All Below. See how things look with this selection added.\n", "* **Add a b-tagging scale factor**. The weight of a simulated event is usually given by $xsec\\_weight*mcWeight*scaleFactor\\_PILEUP*scaleFactor\\_ELE*scaleFactor\\_MUON*scaleFactor\\_LepTRIGGER$. Since we're asking for b-tagged jets, we should also include a scale factor for b-tagging in this multiplication. Uncomment *scaleFactor_BTAG* in line 20 and 136 of '[Can we process the data yet?!](#load_data)'. Uncomment scaleFactor_BTAG in [the calc_weight function](#calc_weight). Cell -> Run All Below. See how things look with this scale factor added.\n", "* **Add data_A**. Uncomment *data_A* in '[Samples to process](#samples_to_process)'. Change *lumi* to 10 in '[Lumi, fraction, file path](#fraction)'. Cell -> Run All Below. See how things look with *data_A* added.\n", "* **Increase the fraction of all events** that are processed in '[Lumi, fraction, file path](#fraction)' (maximum 1). Cell -> Run All Below. See how things look with fraction increased.\n", "* **Increase the fraction of simulated events** that are processed in '[Lumi, fraction, file path](#fraction)' by increasing *MC_to_data_ratio* (maximum 1). Cell -> Run All Below. See how things look with this ratio increased.\n", "* **Add in the other background samples** in '[Samples to process](#samples_to_process)' by uncommenting the files that aren't currently being processed. Cell -> Run All Below. See how things look with all added. Maybe look into which ones don't contain any events that pass our requirements, so that you won't have to process them again.\n", "* **Modify some BDT hyper-parameters** in '[Training Decision Trees (6j2b)](#BDT_6j2b_training)' and/or '[Training Decision Trees (5j2b)](#BDT_5j2b_training)' and/or '[Training Decision Trees (6j1b)](#BDT_6j1b_training)'. Cell -> Run All Below. You may find the [sklearn documentation on GradientBoostingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) helpful.\n", "\n", "With each change, keep an eye on the BDT output distributions in each region." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to contents](#contents)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }