{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring the *wilson* Python package\n",
"\n",
"More information about the package can be foud on the [project web site](https://wilson-eft.github.io).\n",
"\n",
"Execute cells with Shift + Enter.\n",
"\n",
"Insert a new cell below by pressing 'b'."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import wilson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examples from the paper"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"mywilson = wilson.Wilson({'uG_33': 1e-6},\n",
" scale=1e3, eft='SMEFT', basis='Warsaw')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a Jupyter notebook the `Wilson` instance is \"pretty printed\" in the form of a table showing the input EFT, basis and scale, as well as a table with the input Wilson coefficient values."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
Wilson coefficients
\n",
"\n",
"\n",
" \n",
" \n",
" EFT | \n",
" Basis | \n",
" scale | \n",
"
\n",
" \n",
" \n",
" \n",
" SMEFT | \n",
" Warsaw | \n",
" 1000.0 GeV | \n",
"
\n",
" \n",
"
\n",
"Values
\n",
"\n",
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Re | \n",
" Im | \n",
"
\n",
" \n",
" \n",
" \n",
" uG_33 | \n",
" 0.000001 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mywilson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running down to the top mass scale, look at the induced values of $C_{uG}^{33}$ and $C_{uB}^{33}$"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1.0228430603379855e-06-1.572304908340795e-19j),\n",
" (-1.9807848899202916e-08+3.044024654195142e-21j))"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wc = mywilson.match_run(scale=160, eft='SMEFT', basis='Warsaw')\n",
"wc['uG_33'], wc['uB_33']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$R_{D^*}$ as computed by [flavio](https://flav-io.github.io) induced by $(C_{lq}^{(3)})_{3333}$"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8847548062233653"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import flavio\n",
"\n",
"my_wilson = wilson.Wilson({'lq3_3333': 1e-6},\n",
" scale=1e3, eft='SMEFT', basis='Warsaw')\n",
"\n",
"RDs_SM = flavio.sm_prediction('Rtaul(B->D*lnu)')\n",
"RDs_NP = flavio.np_prediction('Rtaul(B->D*lnu)', my_wilson)\n",
"\n",
"RDs_NP / RDs_SM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}