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@DavidMStraub
Created October 13, 2016 18:13
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import flavio\n",
"import flavio.statistics.fits\n",
"import flavio.plots\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from numpy import cos, sin\n",
"%matplotlib inline\n",
"plt.rc('text', usetex=True)\n",
"plt.rcParams['savefig.dpi'] = 100\n",
"plt.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-1.6933130732911199e-10+6.3442916109792175e-12j)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CSM = flavio.physics.mesonmixing.wilsoncoefficient.cvll_d(\n",
" par=flavio.default_parameters.get_central_all(),\n",
" meson='Bs',\n",
" scale = 4.2)[0]\n",
"CSM"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def wc_fct(Cr, Ci):\n",
" return { 'CVLL_bsbs': (Cr + 1j*Ci) * CSM }"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"observables = ['DeltaM_s', 'S_psiphi']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"par_future = flavio.default_parameters.copy()\n",
"par_future.set_constraint('bag_B0_1', '0.9130(45)')\n",
"par_future.set_constraint('bag_Bs_1', '0.9520(47)')\n",
"par_future.set_constraint('f_B0', '0.1920(10)')\n",
"par_future.set_constraint('f_Bs', '0.2284(11)')\n",
"par_future.set_constraint('Vub', '3.62(3)e-3')\n",
"par_future.set_constraint('Vcb', '4.221(42)e-2')\n",
"par_future.set_constraint('gamma', '1.270(15)') # 0.5°"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m = flavio.Measurement('future phi_s')\n",
"m.set_constraint('S_psiphi', '0.033+-0.008')\n",
"m.set_constraint('S_psiK', '0.679 ± 0.008')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def my_fit(name, observables):\n",
" return flavio.statistics.fits.FastFit(name,\n",
" par_obj = par_future,\n",
" fit_parameters = [],\n",
" nuisance_parameters = flavio.default_parameters.all_parameters,\n",
" observables = observables,\n",
" fit_wc_names = wc_fct.__code__.co_varnames,\n",
" fit_wc_function = wc_fct,\n",
" input_scale = 4.2,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fit={}\n",
"for obs in observables:\n",
" fit[obs] = my_fit('CVLL ' + obs + ' future', [obs] )\n",
"fit['global'] = my_fit('CVLL Bs mixing future', observables)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2min 45s, sys: 80 ms, total: 2min 45s\n",
"Wall time: 3min 26s\n"
]
}
],
"source": [
"%%time\n",
"for f in fit.values():\n",
" f.make_measurement(N=200)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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pVbpduOwWrppyndopI2qREyRQCyGmpJRisSfPlAlllcUuLlzvz3Q3hJiRBGoh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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0fbe887400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(5,5))\n",
"xmax=0.12\n",
"x0=-0.1\n",
"ymax=0.05\n",
"flavio.plots.band_plot(fit['DeltaM_s'].log_likelihood, x0-xmax, x0+xmax, -xmax, xmax, n_sigma=1, col=1,\n",
" contourf_args={'alpha':0.5}, label=r'$\\Delta M_s$ ($1\\sigma$)')\n",
"flavio.plots.band_plot(fit['S_psiphi'].log_likelihood, x0-xmax, x0+xmax, -ymax, ymax, n_sigma=1, col=2,\n",
" contourf_args={'alpha':0.5}, label=r'$S_{\\psi \\phi}$ ($1\\sigma$)')\n",
"flavio.plots.band_plot(fit['global'].log_likelihood, x0-xmax, x0+xmax, -ymax, ymax, n_sigma=1, col=0,\n",
" label='global')\n",
"flavio.plots.band_plot(fit['global'].log_likelihood, x0-xmax, x0+xmax, -ymax, ymax, n_sigma=(2,3), \n",
" contourf_args={'alpha':0.1}, contour_args={'alpha':0.4}, col=0)\n",
"flavio.plots.flavio_branding(x=0.77, y=0.09)\n",
"plt.legend()\n",
"plt.xlim([x0-xmax,x0+xmax])\n",
"plt.ylim([-xmax,xmax])\n",
"plt.axhline(0, c='k', lw=0.2)\n",
"plt.axvline(0, c='k', lw=0.2)\n",
"plt.xlabel(r'$\\text{Re} ( C_{VLL}/C_{VLL}^\\text{SM} ) $')\n",
"plt.ylabel(r'$\\text{Im} ( C_{VLL}/C_{VLL}^\\text{SM} ) $')\n",
"plt.tight_layout()\n",
"plt.savefig('CVLL-Bs-future.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
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