FastFit_WilsonCoefficients_CS_CSp.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Constraints on scalar operators from $B_s\\to\\mu^+\\mu^-$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot from the paper [arXiv:1702.05498](https://arxiv.org/abs/1702.05498) by W. Altmannshofer, C. Niehoff, and D. M. Straub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and notebook settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.24'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import flavio\n",
    "import flavio.plots\n",
    "import flavio.statistics.fits\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "flavio.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wilson coefficient function\n",
    "def wc_fct_smeft(CS, CSp):\n",
    "    return {\n",
    "            'CS_bsmumu': CS,\n",
    "            'CP_bsmumu': -CS,\n",
    "            'CSp_bsmumu': CSp,\n",
    "            'CPp_bsmumu': CSp,\n",
    "           }\n",
    "\n",
    "fast_fit = flavio.statistics.fits.FastFit(\n",
    "                name = \"CS-CSp SMEFT fast fit\",\n",
    "                nuisance_parameters = 'all',\n",
    "                observables = [ 'BR(Bs->mumu)', 'BR(B0->mumu)', ],\n",
    "                fit_wc_function = wc_fct_smeft,\n",
    "                input_scale = 4.8,\n",
    "                include_measurements = ['LHCb Bs->mumu 2017', 'CMS Bs->mumu 2013'],\n",
    "            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fast fits: make pseudo measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"fast fits\" combine correlated experimental and theoretical uncertainties into one big \"pseudo-mesurement\". This takes a few minutes. But once this is done, plotting is blazing fast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 35.9 s, sys: 141 ms, total: 36 s\n",
      "Wall time: 38.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "fast_fit.make_measurement()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constraint on the real parts of $C_S$ and $C_S'$, imposing the SMEFT relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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   "source": [
    "# setting some options\n",
    "plt.figure(figsize=(4,4))\n",
    "opt = dict(x_min=-0.05, x_max=0.02, y_min=-0.05, y_max=0.02, n_sigma=(1,2), interpolation_factor=3, steps=30)\n",
    "\n",
    "# plot!\n",
    "flavio.plots.likelihood_contour(fast_fit.log_likelihood, col=0, **opt)\n",
    "\n",
    "# labels, legend, etc.\n",
    "flavio.plots.flavio_branding(y=0.07, x=0.05)\n",
    "plt.gca().set_aspect(1)\n",
    "plt.axhline(0, c='k', lw=0.2)\n",
    "plt.axvline(0, c='k', lw=0.2)\n",
    "plt.xlabel(r'$\\mathrm{Re}\\,C_S=-\\mathrm{Re}\\,C_P ~[\\mathrm{GeV}^{-1}]$')\n",
    "plt.ylabel(r'$\\mathrm{Re}\\,C_S^\\prime= \\mathrm{Re}\\,C_P^\\prime ~[\\mathrm{GeV}^{-1}]$');"
   ]
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