{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "NpbbMzECbvTv" }, "source": [ "# Instrumental Noise in _Kepler_ and _K2_ #1: Data Gaps and Quality Flags" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "L57_f2QscO85" }, "source": [ "## Learning Goals\n", "\n", "By the end of this tutorial, you will:\n", "\n", "- Have a working knowledge of _Kepler_ quality flags and be able to access them in light curve and TPF data.\n", "- Be able to identify the cause of various types of gaps in the data.\n", "- Understand the most common reasons for individual cadence exclusions." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "D0ixWhW-cTfm" }, "source": [ "## Introduction\n", "\n", "This notebook is the first part of a series on identifying instrumental and systematic sources of noise in _Kepler_ and _K2_ data. In this tutorial, we will look at practical examples of data gaps and single-cadence quality issues in _Kepler_ data, and learn to identify their causes. Assumed knowledge for this tutorial is a good familiarity with light curve and target pixel file (TPF) data products, and the ability to work with their metadata." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "f0yfyjrD2ERM" }, "source": [ "## Imports\n", "\n", "We'll use **[Lightkurve](https://docs.lightkurve.org/)** for downloading and handling _Kepler_ data throughout this tutorial. We'll also use **[NumPy](https://numpy.org/)** to perform a few auxiliary functions, and **[Matplotlib](https://matplotlib.org/)** for plotting." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "execution": { "iopub.execute_input": "2023-11-03T14:13:55.465103Z", "iopub.status.busy": "2023-11-03T14:13:55.464324Z", "iopub.status.idle": "2023-11-03T14:13:56.766414Z", "shell.execute_reply": "2023-11-03T14:13:56.766106Z" }, "id": "LQ6s2Mlwc4l9" }, "outputs": [], "source": [ "import lightkurve as lk\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fInD_fMu2pmx" }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XMPMikC-cq3N" }, "source": [ "## 1. Background\n", "\n", "The _Kepler_ space telescope observed the same patch of sky for four continuous years, between 2009 and 2013. Over the course of 18 observing quarters, it collected light curves and pixel data for 150,000 stars in 30-minute Long Cadence mode, and 512 stars per quarter in one-minute Short Cadence mode. Following the failure of two of the telescope's four reaction wheels, the telescope continued as the _K2_ mission, which observed along the ecliptic plane for 20 campaigns.\n", "\n", "In this tutorial, we'll explore some quality issues that arose during both the _Kepler_ and _K2_ missions, and learn how to identify and mitigate them. It's important to note that many of these issues only appear in the calibrated pixels or simple aperture photometry (SAP) data, and more still are removed by a quality masking process which we'll discuss in the next section. If you're working with presearch data conditioning SAP (PDCSAP) light curves, you won't run into many of these issues." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_v8jLrQBctUM" }, "source": [ "### 1.1 Quality flags\n", "\n", "Before we look at some practical examples in time series data, let's familiarize ourselves with how data gaps and single-cadence quality events are identified in _Kepler_ data files. Every _Kepler_ FITS file has a [QUALITY](https://docs.lightkurve.org/reference/api/lightkurve.KeplerTargetPixelFile.quality.html?highlight=quality#lightkurve.KeplerTargetPixelFile.quality) column, which contains a quality flag for each individual data cadence. These flags comprise of one or more binary bits, which are expressed as an integer. A handy reference document for _Kepler_'s quality flags is Table 2-3 in the [MAST Kepler Archive Manual](https://archive.stsci.edu/files/live/sites/mast/files/home/missions-and-data/k2/_documents/MAST_Kepler_Archive_Manual_2020.pdf).\n", "\n", "Let's start by downloading the data we'll work with throughout this tutorial:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", "execution": { "iopub.execute_input": "2023-11-03T14:13:56.768509Z", "iopub.status.busy": "2023-11-03T14:13:56.768263Z", "iopub.status.idle": "2023-11-03T14:13:57.878700Z", "shell.execute_reply": "2023-11-03T14:13:57.878319Z" }, "id": "KORJOZglc-Lx" }, "outputs": [], "source": [ "lc = lk.search_lightcurve('KIC 2436365', author='Kepler', quarter=2).download()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5ceFMui-ASy2" }, "source": [ "By default, Lightkurve downloads quality-masked data. This means that a variety of cadences with a non-zero quality flag will already be removed from the light curves or TPFs you download using the instruction above.\n", "\n", "For this tutorial, we are also going to download some TPF data with no quality mask applied. We can do this by passing the optional `quality_bitmask=0` argument to the [download()](https://docs.lightkurve.org/reference/search.html?highlight=download) method as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", "execution": { "iopub.execute_input": "2023-11-03T14:13:57.880935Z", "iopub.status.busy": "2023-11-03T14:13:57.880741Z", "iopub.status.idle": "2023-11-03T14:13:58.621213Z", "shell.execute_reply": "2023-11-03T14:13:58.620906Z" }, "id": "h695JEhsASF7" }, "outputs": [], "source": [ "tpf = lk.search_targetpixelfile('KIC 2436365', author='Kepler', quarter=2).download(quality_bitmask=0)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "BRbY5HdRDlZ6" }, "source": [ "Passing `quality_bitmask=0` has the effect of including every cadence in the data, even those with serious quality issues or NaN ([not a number](https://en.wikipedia.org/wiki/NaN)) values in the flux. This is not necessarily recommended when using Lightkurve, but here it will allow us to explore a wide variety of data quality issues, many of which will be useful to know about if you're working directly with FITS files from MAST.\n", "\n", "Now that we have our data, let's have a look at the range of quality flags present. Remember, this is the unmasked data, so every flagged cadence is included.\n", "\n", "Here, we're using the NumPy function [`unique()`](https://numpy.org/doc/stable/reference/generated/numpy.unique.html), which takes an array as its input and returns every unique value:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 85 }, "colab_type": "code", "execution": { "iopub.execute_input": "2023-11-03T14:13:58.623119Z", "iopub.status.busy": "2023-11-03T14:13:58.622997Z", "iopub.status.idle": "2023-11-03T14:13:58.625683Z", "shell.execute_reply": "2023-11-03T14:13:58.625415Z" }, "executionInfo": { "elapsed": 20120, "status": "ok", "timestamp": 1600725930587, "user": { "displayName": "Geert Barentsen", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gj8sjdnDeqdejfe7OoouYPIclAQV0KSTpsU469Jyeo=s64", "userId": "05704237875861987058" }, "user_tz": 420 }, "id": "E8QMKBgHetS_", "outputId": "945b812b-fee4-4ee1-dc2d-c065937852ad" }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 16, 64, 128, 144, 256, 4160,\n", " 8192, 8208, 8320, 12352, 16384, 32772, 32778, 32800,\n", " 32804, 32816, 98305, 98312, 98314, 393216, 393232, 393344,\n", " 401408, 409600], dtype=int32)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.unique(tpf.quality)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "B4Jrg5JMEBM-" }, "source": [ "You'll notice that some of these appear to be integers that correspond to bits — that is, powers of two — but others are additive. This indicates that multiple quality issues are present in a particular cadence.\n", "\n", "We can use Lightkurve's `decode()` function for accessing the information stored in each quality flag:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 459 }, "colab_type": "code", "execution": { "iopub.execute_input": "2023-11-03T14:13:58.627263Z", "iopub.status.busy": "2023-11-03T14:13:58.627158Z", "iopub.status.idle": "2023-11-03T14:13:58.629662Z", "shell.execute_reply": "2023-11-03T14:13:58.629410Z" }, "executionInfo": { "elapsed": 20109, "status": "ok", "timestamp": 1600725930588, "user": { "displayName": "Geert Barentsen", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gj8sjdnDeqdejfe7OoouYPIclAQV0KSTpsU469Jyeo=s64", "userId": "05704237875861987058" }, "user_tz": 420 }, "id": "m4VWYkUyevyC", "outputId": "b60a4dc9-8489-45b4-e567-3ff7705cb05c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 []\n", "1 ['Attitude tweak']\n", "16 ['Zero crossing']\n", "64 ['Argabrightening']\n", "128 ['Cosmic ray in optimal aperture']\n", "144 ['Zero crossing', 'Cosmic ray in optimal aperture']\n", "256 ['Manual exclude']\n", "4160 ['Argabrightening', 'Argabrightening on CCD']\n", "8192 ['Cosmic ray in collateral data']\n", "8208 ['Zero crossing', 'Cosmic ray in collateral data']\n", "8320 ['Cosmic ray in optimal aperture', 'Cosmic ray in collateral data']\n", "12352 ['Argabrightening', 'Argabrightening on CCD', 'Cosmic ray in collateral data']\n", "16384 ['Detector anomaly']\n", "32772 ['Coarse point', 'No fine point']\n", "32778 ['Safe mode', 'Earth point', 'No fine point']\n", "32800 ['Desaturation event', 'No fine point']\n", "32804 ['Coarse point', 'Desaturation event', 'No fine point']\n", "32816 ['Zero crossing', 'Desaturation event', 'No fine point']\n", "98305 ['Attitude tweak', 'No fine point', 'No data']\n", "98312 ['Earth point', 'No fine point', 'No data']\n", "98314 ['Safe mode', 'Earth point', 'No fine point', 'No data']\n", "393216 ['Rolling band in optimal aperture', 'Rolling band in full mask']\n", "393232 ['Zero crossing', 'Rolling band in optimal aperture', 'Rolling band in full mask']\n", "393344 ['Cosmic ray in optimal aperture', 'Rolling band in optimal aperture', 'Rolling band in full mask']\n", "401408 ['Cosmic ray in collateral data', 'Rolling band in optimal aperture', 'Rolling band in full mask']\n", "409600 ['Detector anomaly', 'Rolling band in optimal aperture', 'Rolling band in full mask']\n" ] } ], "source": [ "for flag in np.unique(tpf.quality):\n", " print(flag, lk.KeplerQualityFlags.decode(flag))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "haTYda77r4X1" }, "source": [ "We can use Python's \"bitwise and\" operator (&) to select the cadence numbers affected by a specific quality flag as follows:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "execution": { "iopub.execute_input": "2023-11-03T14:13:58.631147Z", "iopub.status.busy": "2023-11-03T14:13:58.631039Z", "iopub.status.idle": "2023-11-03T14:13:58.633396Z", "shell.execute_reply": "2023-11-03T14:13:58.633140Z" }, "executionInfo": { "elapsed": 20098, "status": "ok", "timestamp": 1600725930589, "user": { "displayName": "Geert Barentsen", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gj8sjdnDeqdejfe7OoouYPIclAQV0KSTpsU469Jyeo=s64", "userId": "05704237875861987058" }, "user_tz": 420 }, "id": "XH6tC4x5rwZa", "outputId": "33b27a6f-a12b-4154-e1e4-25103b804b14" }, "outputs": [ { "data": { "text/plain": [ "array([4060, 5767, 6432, 6796, 6797], dtype=int32)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tpf.cadenceno[(tpf.quality & 64) > 0] # cadence numbers flagged for \"Argabrightening\" (flag 64)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Pd3VEVtyFLzh" }, "source": [ "In the following sections, we'll walk through the majority of these data quality events, and look at practical examples in the light curve and TPF we downloaded above.\n", "\n", "Some of these flags are not covered in this tutorial, such as rolling bands. For more information on other data quality issues, see other tutorials in this series, as well as the various [_Kepler_ Data Handbooks](https://archive.stsci.edu/missions-and-data/kepler/documents)." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "HXfCyGQSeiNi" }, "source": [ "## 2. Common Data Gaps\n", "\n", "The nominal _Kepler_ mission observed one area continuously between 2009 and 2013 — but of course, there were various necessary breaks in that continuity. For example, the telescope rotated at the end of each quarter, which necessitated a break in data collection.\n", "\n", "We can use quality flags to identify these various events, but often it's more convenient to check the _Kepler_ Mission Timeline, from the [_Kepler_ Data Characteristics Handbook](https://archive.stsci.edu/files/live/sites/mast/files/home/missions-and-data/kepler/_documents/Data_Characteristics.pdf):\n", "\n", "![Kepler Mission Timeline from the Data Characteristics Handbook, a calendar showing Quarters 0–17 from 2009–13, with major events and data gaps marked.](kepler-mission-timeline.png)\n", "\n", "Throughout this section we'll explore the most common reasons for gaps in _Kepler_ data: monthly data downlinks, safe modes, and coarse pointing/loss of fine pointing. Though we're looking at _Kepler_ data here, these data gaps can also be found in _K2_ mission data." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LCcNWywldDnY" }, "source": [ "### 2.1 Monthly data downlink\n", "\n", "During the _Kepler_ mission, the telescope changed its orientation once a month to point at Earth and downlink the last month of data. This caused a gap in the time series data, as the telescope could not collect while it was downlinking. When the telescope returned to regular pointing, its motion induced what is known as a \"thermal transient.\" This means that the telescope components and detector electronics underwent a temperature change, and the electron count reading was temporarily increased. A change in temperature can slightly change the telescope focus. This manifests in the simple aperture photometry (SAP) data as a downward slope caused by \"reheating,\" while the flux returns to its previous level. The thermal transient is corrected in PDCSAP data; there is no quality flag for data affected by a thermal transient. ([_Kepler_ Data Characteristics Handbook](https://archive.stsci.edu/files/live/sites/mast/files/home/missions-and-data/kepler/_documents/Data_Characteristics.pdf), Section 5.5.)\n", "\n", "The following code uses Matplotlib to zoom in on an Earth pointing event and highlight the affected section of the SAP light curve:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 394 }, "colab_type": "code", "execution": { "iopub.execute_input": "2023-11-03T14:13:58.635154Z", "iopub.status.busy": "2023-11-03T14:13:58.635044Z", "iopub.status.idle": "2023-11-03T14:13:58.854715Z", "shell.execute_reply": "2023-11-03T14:13:58.854422Z" }, "executionInfo": { "elapsed": 20086, "status": "ok", "timestamp": 1600725930590, "user": { "displayName": "Geert Barentsen", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gj8sjdnDeqdejfe7OoouYPIclAQV0KSTpsU469Jyeo=s64", "userId": "05704237875861987058" }, "user_tz": 420 }, "id": "2V7CICdpdIMP", "outputId": "e8d18550-9698-49e0-8fdd-54a71f95205c" }, "outputs": [ { "data": { "image/png": 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", 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