Tutorial-DarkPhoton.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FORESEE- Dark Photon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from src.foresee import Foresee, Utility, Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Initialization \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All function that we will need are included in the FORESEE package. We start by simply initializing it: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "foresee = Foresee()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If dark photons are sufficiently light, they are mainly produced via pion decay. To start, let us have a look at the pion spectrum in terms of the angle with respect to the beam axis $\\theta$ and the momentum $p$. This can be done using the function `get_spectrumplot` which requires the MC particle ID (or simply pid), the MC generator and the energy. The units on the coloraxis are pb/bin. \n",
    "\n",
    "FORESEE provides the 2D spectrum as tables for a variety of particles ($\\pi^0$, $\\eta$, ...), generators (SIBYLL, EPOSLHC, QGSJET, PYTHIA) and collision energies (14, 27 and 100 TeV). The datafiles are stored in the directory `files/hadrons`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot=foresee.get_spectrumplot(pid=\"111\", generator=\"EPOSLHC\", energy=\"14\")\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Specifying the Model: Dark Photons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The phenomenology of the dark photon $A'$ can be described by the following Lagrangian\n",
    "\n",
    "\\begin{equation}\n",
    " \\mathcal{L} = \\frac{1}{2} m_{A'}^2 A'^2  - \\epsilon e \\sum \\bar f \\gamma^\\mu f A'_\\mu\n",
    "\\end{equation}\n",
    "\n",
    "with the dark photon mass $m_{A'}$ and the kinetic mixing parameter $\\epsilon$ as free parameters. For the search for dark photons at forward experiments we need to know i) the *production rate*, ii) the *lifetime* and iii) possibly the *decay branching fractions* of dark photons as function of those two parameters. All properties are specified in the `Model` class. We initialize it with the name of the model as argument. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy = \"14\"\n",
    "modelname=\"DarkPhoton\"\n",
    "model = Model(modelname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Production** Most LLPs are produced in the decay of primary particles. Here we need to specify the total branching fraction into the LLP as function of the `coupling` and the `mass`. We additionally specify the variable `nsample` which specifies how often each parent particle is decayed. \n",
    "\n",
    "For the dark photon, we have the branching fractions\n",
    "\n",
    "\\begin{equation}\n",
    "    \\text{BR}(\\pi^0 \\to A' \\gamma) = 2 \\epsilon^2 \\times\\text{BR}(\\pi^0 \\to \\gamma\\gamma) \\times \\left(1-m_{A'}^2/m_\\pi^2\\right)^3\n",
    "    \\quad\\quad\\text{and}\\quad\\quad\n",
    "    \\text{BR}(\\eta \\to A' \\gamma) = 2 \\epsilon^2 \\times\\text{BR}(\\eta \\to \\gamma\\gamma) \\times \\left(1-m_{A'}^2/m_\\eta^2\\right)^3\n",
    "\\end{equation}\n",
    "\n",
    "There is also the option for 3-body decays $p_0 \\to p_1 p_2 p_3$ with $p_3$ being the LLP. This can be added using the function `add_production_3bodydecay()`. It requires to provide `br` which is the differential branching fraction $d\\text{BR}/(dq \\ d\\cos\\vartheta)$ where $q^2=(p_2+p_3)^2$ and $\\vartheta$ is the angle between $p_3$ in the restframe of $p_2+p_3$ and the direction of $p_2+p_3$ in the restframe of $p_0$. We do not have an example for this in the dark photon case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add_production_2bodydecay(\n",
    "    pid0 = \"111\",\n",
    "    pid1 = \"22\",\n",
    "    br = \"2.*0.99 * coupling**2 * pow(1.-pow(mass/self.masses('111'),2),3)\",\n",
    "    generator = \"EPOSLHC\",\n",
    "    energy = energy,\n",
    "    nsample = 10\n",
    ")\n",
    "\n",
    "model.add_production_2bodydecay(\n",
    "    pid0 = \"221\",\n",
    "    pid1 = \"22\",\n",
    "    br = \"2.*0.39 * coupling**2 * pow(1.-pow(mass/self.masses('221'),2),3)\",\n",
    "    generator = \"EPOSLHC\",\n",
    "    energy = energy,\n",
    "    nsample = 10, \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also produce the dark photon via it's resonant mixing with the SM vector mesons, in particular the $\\omega$, $\\rho$ and $\\phi$ mesons. Following [arXiv:1810.01879](https://arxiv.org/pdf/1810.01879.pdf), we can write \n",
    "\n",
    "\\begin{equation}\n",
    "    \\sigma(A') = \\theta_V^2 \\  \\sigma(V)\n",
    "    \\quad\\quad\\text{with}\\quad\\quad\n",
    "    \\theta_V = \\frac{\\epsilon e}{g_V}  \\frac{m_V^2}{m_{A'}^2 - m_V^2 + i m_V \\Gamma_V}\n",
    "\\end{equation}\n",
    "\n",
    "Here $g_\\omega = 17$, $g_\\rho=5$ and $g_\\phi=-12.88$. We can specify this production mode using `model.add_production_mixing()`. In the following, we focus on the $\\rho$, which provides the leading contribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add_production_mixing(\n",
    "    pid = \"113\",\n",
    "    mixing = \"coupling * 0.3/5. * 0.77545**2/abs(mass**2-0.77545**2+0.77545*0.147*1j)\",\n",
    "    generator = \"EPOSLHC\",\n",
    "    energy = energy,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Particles can also be produced directly in collisions. In the case of dark photons, this proceeds through Bremsstrahlung at lower masses or through Drell-Yan production at higher masses. The spectra for LLPs at some reference coupling need to be provides in the `files/direct` directory. We also provide an option to implement additional conditions in terms of the momentum `p`, in this case the requirement that pT of dark photon produced in Bremssteahlung is smaller than 1 GeV.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add_production_direct(\n",
    "    label = \"Brem\",\n",
    "    energy = energy,\n",
    "    condition = \"p.pt<1\",\n",
    "    coupling_ref=1,\n",
    ")\n",
    "\n",
    "model.add_production_direct(\n",
    "    label = \"DY\",\n",
    "    energy = energy,\n",
    "    coupling_ref=1,\n",
    "    massrange=[1.5, 10.]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lifetime:** For most models the lifetime can be written as $\\ c\\tau(m,g)\\  =\\  c\\tau(m,g_*) \\ \\cdot\\  g_*^2 \\ / \\ g^{2}$ where $m$ is the mass, $g$ is the coupling and $g_*$ is some reference coupling. In this case, we can provide a table of masses $m$ and lifetimes $c\\tau(m,g_*)$ at a reference coupling $g_*$ using `model.set_ctau_1d`. Alternatively, one can also provide a table of masses $m$, couplings $g$ and lifetimes $c\\tau(m,g)$ via `model.set_ctau_2d()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.set_ctau_1d(\n",
    "    filename=\"files/models/\"+modelname+\"/ctau.txt\", \n",
    "    coupling_ref=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Branching Fractions:** For most models, the branching fractions into particular decay channels are independent of the overall couplings. In this case, we can provide a set of tables of masses $m$ and branching fractions $BR(m)$ using `model.set_br_1d`. Alternatively, we can also provide table of masses $m$, couplings $g$ and brnaching fraction $BR(m,g)$ via `model.set_br_2d()`. In both cases, we need to specify a list of decay modes and a list of filenames with the corresponding data tables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.set_br_1d(\n",
    "    modes=[\"e_e\", \"mu_mu\"],\n",
    "    filenames=[\"files/models/\"+modelname+\"/br/e_e.txt\",\"files/models/\"+modelname+\"/br/mu_mu.txt\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Generate LLP Spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we have specified the model, let us generate the LLP spectra. We first set the model we just created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "foresee.set_model(model=model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first look at one benchmark scenario with $m_{A'}=100$ MeV and $\\epsilon=10^{-5}$ using the function `foresee.get_llp_spectrum()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.1.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.1.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.1.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.1.npy')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt = foresee.get_llp_spectrum(0.1, coupling=10**(-5), do_plot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now produce a set of LLP spectra for various masses  (chosen to be equally spaced in log(mass)) and unit coupling and save them.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.01.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.01.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.01.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.01.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.0126.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.0126.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.0126.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.0126.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.0158.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.0158.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.0158.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.0158.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.02.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.02.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.02.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.02.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.0251.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.0251.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.0251.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.0251.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.0316.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.0316.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.0316.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.0316.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.0398.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.0398.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.0398.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.0398.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.0501.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.0501.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.0501.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.0501.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.0631.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.0631.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.0631.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.0631.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.0794.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.0794.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.0794.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.0794.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.1.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.1.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.1.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.1.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.1122.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.1122.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.1122.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.1122.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.1259.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_111_m_0.1259.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.1259.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.1259.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.1413.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.1413.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.1413.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.1585.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.1585.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.1585.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.1778.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.1778.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.1778.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.1995.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.1995.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.1995.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.2239.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.2239.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.2239.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.2512.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.2512.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.2512.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.2818.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.2818.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.2818.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.3162.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.3162.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.3162.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.3548.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.3548.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.3548.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.3981.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.3981.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.3981.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.4467.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.4467.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.4467.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.5012.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.5012.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_221_m_0.5012.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.5623.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.5623.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.6026.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.6026.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.631.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.631.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.6457.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.6457.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.6607.npy')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.6607.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.6761.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.6761.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.6918.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.6918.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.7079.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.7079.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.7244.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.7244.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.7413.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.7413.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.7586.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.7586.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.7762.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.7762.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.7943.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.7943.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.8128.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.8128.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.8318.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.8318.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.8511.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.8511.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.871.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.871.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.8913.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.8913.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.912.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.912.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.9333.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.9333.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.955.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.955.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_0.9772.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_0.9772.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_1.0.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_1.0.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_1.122.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_1.122.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_1.2589.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_1.2589.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_1.4125.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_1.4125.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_1.5849.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_113_m_1.5849.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_1.5849.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_1.7783.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_1.7783.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_1.9953.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_1.9953.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_2.2387.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_2.2387.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_2.5119.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_2.5119.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_2.8184.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_2.8184.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_3.1623.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_3.1623.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_3.9811.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_3.9811.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_5.0119.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_5.0119.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_6.3096.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_6.3096.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_7.9433.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_7.9433.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_Brem_m_10.0.npy')\n",
      "('save data to file:', 'files/models/DarkPhoton/LLP_spectra/14TeV_DY_m_10.0.npy')\n"
     ]
    }
   ],
   "source": [
    "masses = [ \n",
    "    0.01  ,  0.0126,  0.0158,  0.02  ,  0.0251,  0.0316,  0.0398,\n",
    "    0.0501,  0.0631,  0.0794,  0.1   ,  0.1122,  0.1259,  0.1413,\n",
    "    0.1585,  0.1778,  0.1995,  0.2239,  0.2512,  0.2818,  0.3162,\n",
    "    0.3548,  0.3981,  0.4467,  0.5012,  0.5623,  0.6026,  0.631 ,\n",
    "    0.6457,  0.6607,  0.6761,  0.6918,  0.7079,  0.7244,  0.7413,\n",
    "    0.7586,  0.7762,  0.7943,  0.8128,  0.8318,  0.8511,  0.871 ,\n",
    "    0.8913,  0.912 ,  0.9333,  0.955 ,  0.9772,  1.    ,  1.122 ,\n",
    "    1.2589,  1.4125,  1.5849,  1.7783,  1.9953,  2.2387,  2.5119,\n",
    "    2.8184,  3.1623,  3.9811,  5.0119,  6.3096,  7.9433, 10.    \n",
    "]\n",
    "for mass in masses:\n",
    "    foresee.get_llp_spectrum(mass=mass,coupling=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Count Eventrate in Detector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before counting the number of decays within the detector volume, let us specify the detector geometry. For this we need\n",
    "\n",
    "- the `distance` from the IP in meter (default: 480)\n",
    "- the `length` in meter (default: 5)\n",
    "- the `luminosity` in units of fb^-1 (default: 3000)\n",
    "- the `selection` depending in `x.x` and `x.y` (default: `np.sqrt(x.x**2 + x.y**2)< 1`)\n",
    "- the decay `channels` which the detector can see (default: None, meaning all of them)\n",
    "\n",
    "The default correspond to the FASER2 detector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance, selection, length, luminosity, channels = 480, \"np.sqrt(x.x**2 + x.y**2)< 1\", 5, 3000, None\n",
    "foresee.set_detector(distance=distance, selection=selection, length=length, luminosity=luminosity, channels=channels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look for one specific dark photon mass $m_{A'}=100$ MeV at how many particle decay inside the decay volume. This can be done using `get_events()` for which we need to specify again the mass and filename. Additionally, we can specify \n",
    "\n",
    "- the `couplings` for which we evaluate the event rate as numpy array (deault `np.logspace(-8,-3,51)`)\n",
    "- the `preselectioncuts` when reading the LLP spectra in terms of momentum `p` and angle `th` (default: `th<0.01 and p>100`)\n",
    "- the number `nsample` describing how many randomly choosen values of $\\phi_{LLP}$ should sampled (default 1). This is only relevant for detectors that are not cylyndrical or not centered around the beam axis. \n",
    "- the production `modes` which should be considered\n",
    "- the `energy` of the experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('epsilon =', '1.000e-08', ': nsignal =', '2.140e-06')\n",
      "('epsilon =', '1.000e-07', ': nsignal =', '2.134e-02')\n",
      "('epsilon =', '1.000e-06', ': nsignal =', '1.598e+02')\n",
      "('epsilon =', '1.000e-05', ': nsignal =', '1.402e+03')\n",
      "('epsilon =', '1.000e-04', ': nsignal =', '1.312e-31')\n",
      "('epsilon =', '1.000e-03', ': nsignal =', '0.000e+00')\n"
     ]
    }
   ],
   "source": [
    "mass = 0.1 \n",
    "output = foresee.get_events(mass=0.1, energy=energy, couplings=np.logspace(-8,-3,6), )\n",
    "coups, ctaus, nsigs, energies, weights, _ = output\n",
    "for coup,ctau,nsig in zip(coups, ctaus, nsigs):\n",
    "    print (\"epsilon =\", '{:5.3e}'.format(coup), \": nsignal =\", '{:5.3e}'.format(nsig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous function also outputs the energy distribution of the dark photons which decay inside the detector. Let's have a look at them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nnPVJeQkwAaj0J+D7gKc6EZuZWZ+U91Ow+8/rT3uLh5Q++Z4GzJW0BUkzdWuf8qHA8cBR1QzSzKw3yDuBFrWZ3qqnvSflv7DpgiECvgV8s+wYwONA9tnZZmZmtpn2kvLnKN7OUGZmZn1Weyt63VjDOMzMzOqeV/QyMyuoPAeOedBY7+CkbGZWUHkmUQ8a6x0KuZ+ymZlZPXJSNjMzK4gOk7KkrSSdLWnvWgRkZmZWrzpMyhHxOnA2MLz64ZiZmdWvrAO9Hgf2AR6oYixmZlYlXgK0d8ialGcDP5L0JrAQeJmyhUUioiXn2MzMLCdeArR36MyTMsAc4LttlPEym2ZmZt2QNSl7yU2zOjJt/jSa1jXlVl9X9vI1q0eZkrKX3DSrL03rmrwvsFkP6NSKXpLGAx8AdgJuiIiXJO0OvBwRa6sRoJmZWb3IlJQlbQ3cAMwC3kw/dyfwEslWjiuAL1UpRjMzs7qQdUWvy4CPAFOBIbyzjzIko7E/mXNcZmZmdSdr8/VM4JSIuE9S+SjrPwA75xuWmZkVmXewqo6sSXkg8Oc2zg0BNuQTjpmZ9Qbewao6sjZfPwF8po1zs4BH8gnHzMysfmV9Uv4qcLeke4BbSOYsT5d0KklS/liV4rNSl+8Fq1f0dBSVDRsDp3oKjZlZd2Sdp/yQpKnAhcCVJAO9zgMeAw6KiCeqF2Ivl2ciHTYGGlfnU1feLt8LGoflU5cTvJnVqczzlCPiYWB/SQOBBmCV17vOYPWK4ibSPOWZRPNM8OAkb2a9RqcWD0m9RjJXeX3OsVSFpKuBTwOjI0IdlbcCyDuB5pngzcyqKHNSljQdOIdkRa8BwFuSngS+ERF3VCm+PPwYaCRZ6MTq0bAxblo3s14h64peJwJXAfcCpwCvANuQzF++XdLJEXFtXkGlS3eeAewHTAAeiogDKpQbD1yRllsFXA+cFxEbp2hFxINp2bzCs94mzyRa4KfuPDeR8AYSZj0j65PyV4BrI+LksuPXSLoGOBvILSmTJOLpJAPJtqhUQFIDcA+wFJgB7AZcSjLN65wcYzHrFbyJhFnvlzUpjwB+2sa5nwB/n084Gy2IiNsAJM0HRlYocxLJoiYzI2INyZStoUCjpIvTY2b5yrMpvLU+N4ebWSprUr4PmAzcXeHcZODB3CICIuLtDMUOBu4qS75zgYvSmBbkGZMZ4EFoZlZVWZPyHOB6SSOAn/FOn/KhJMnx+LR/F4CIWJp3oBWMAxaXHoiIFZJa0nOdSsqSTgBOABgzZkxeMZqZmWWWNSm3LnJ6YvoKNt0p6s70p9Jz5ZtWVEMDyeCucs3puSQg6XrSXawkvQjcGRHHl38oIq4DrgOYOHFiVCNgs83k2Ry+q/+YtN7Jm1u8I2tSnlLVKKqoUgI2K4w8m8O9qL/1Ut7c4h1Zl9l8oNqBdEEzUOkRoyE9Z2Zm1qtk3SWqiJaR9B1vJGknYFB6zszMrFfpyjKbRbEIOEPSkIhYmx47kmT5zyI+2ZtVn1cuM+vVCpmUJQ0iWTwEYAdgqKRZ6fuF6UYY1wBfBG6VdBEwlmQ5zcs8R9nqVl6bn3iqllmPKGRSJpludUvZsdb3uwLLI6I53U7ySpLpT6uAy0kSs5mZWa9TyKQcEcvZdMpVW+WWAgdWPSAzM7MayDTQS9L+kmaUvB8p6UeS/kfSpZIqrk9tZmZm2WV9Ur4Y+DlwW/r+u8BUkvWwPwu8TrJphZn1BXmv8Z0nD0KzPixrUt4DOA82DsI6FPhcRMyV9ARJQnZSNusripz0ivrHglkOss5T3hJ4Lf39oyTJ/I70/W+A7XOOy8zMrO5kfVJeRrJ+9P3AMcCjJXODRwP/l39oZn3ftPnTaFrXlEtdowePzqUeM+s5WZPy14FbJP0jydKWM0rOfRL4Rd6BmdWDpnVNPHtcgZuKzaymsq59fbukPYG/Bp6NiN+UnH4UeLoawZmZmdWTrFOiPgOsjoiflCVkgPkkA8HMzMysG7IO9Po3YLc2zu2anjczM7NuyJqU21tdawTgtabNzMy6qc0+5XQFr9IBXV+V9GpZsXcB+wNPVCE2MzOzutLeQK9tgL1K3u8GbFdW5g3gP4ELco7LzMys7rSZlCPiX4F/BZB0H/DPEbGsVoGZmZnVm6xToqZUOxAzM7N6l3nrRkmjgU8BO5L0JZeKiDgzz8DMzMzqTaakLOlQ4MdAf+AVkr7kUgE4KZuZmXVD1iflb5IM6PpsRHidazMzsyrImpR3Ar7ghGxmPS7vvZ69P7MVSNak/AjJUpr3VDEWM7OO5Z1AvT+zFUjWpHwa8ENJfwHuBlaVF4iIljwDMzMzqzdZk/Iz6c9/IxnUVUn/7odjZmZWv7Im5c/RdjI2MzOzHGRdPOTGKsdhZmZW9zIvHgIgaTzwAZLR2DdExEuSdgdejoi11QjQzMysXmRdPGRr4AZgFvBm+rk7gZdI5jCvAL5UpRjNzKonzylWnl5l3ZT1Sfky4CPAVOBh4LWScwtJErKTspn1PnkmUU+vsm7KmpRnAqdExH2SykdZ/wHYOd+wzMzM6k+/jOUGAn9u49wQYEM+4ZiZmdWvrE/KTwCfIelHLjeLZMUvsz5v2vxpNK1ryq2+0YNH51aXmfV+WZPyV4G7Jd0D3EIyZ3m6pFNJkvLHqhSfWaE0rWvi2eM8kMfMqiNT83VEPEQyyGsr4EpAwHnAWOCgiHiiahF2k6SrJf1Rkhc/MTOzQss8TzkiHgb2lzQQaABW9ZL1rn8MNJJM3zIzqx7vYGXdlHWe8ueAn0TE6ohYD6zv6gXTxUbOAPYDJgAPRcQBFcqNB65Iy60CrgfOi4hODSqLiAfT+roasplZNt7Byrop6+jrq4GXJd0u6e8kDe7GNScA04FfA7+pVEBSA8k2kQHMAL4OnE7SZG5mZtYnZU3K2wInA1sCNwKvSJov6TBJ7+rkNRdExE4RcTjwXBtlTiKZhjUzIu6OiGtIEvJpkoa2FpL0lKSVFV4/7GRMZmZmPS7rQK9VEXFDRHwS2J5kf+UGYB5Jgv5B1gtGxNsZih0M3BURa0qOzSVJ1JNL6tonIkZWeB2TNR4zM7OiyPqkvFFE/Dkiro2IqSRNy2uBo3OOaxywrOy6K4CW9JyZmVmf0+mkLGkvSRdI+g1wO7COZFOKPDWQDO4q15yey0zS9ZJeTH9/UdL1bZQ7QdISSUteffXVTgdsZmbWXVlHX+8JHAEcCewBvADcDMyNiKeqF173RcTxGctdB1wHMHHiRM9pNjOzmss6T/k54E8kq3n9Y0Q8Wr2QgOSJuNJcgIb0nJmZWZ+TNSlPAR6MiFo9QS6jrO9Y0k7AIMr6ms3M+izv9Vx3MiXliHigrXOSBgDbRER+q/TDIuAMSUMiYm167EiSRUvajMXMrE/xXs91p82BXpLekLRvyft+khZLek9Z0Q+Q9DFnImmQpFmSZgE7AKNa30salBa7BngduFXSQZJOIFkq87KyaVJmZmZ9RntPygNINp5oJeAAkv2Tu2Mbkr7pUq3vdwWWR0SzpKkkm18sIBmJfTlJYjYzM+uTMm9IkZeIWM6myb6tckuBA6sekJmZWUF0ep6ymZmZVYeTspmZWUF01Hz9BUl/Sn9vbXI+RdLLJWW2zz8sMzOz+tNeUl4BTCo79gfgY22UNTMzs25oMylHxC41jMPMzKzu1Xz0tZmZ9YA8Vwdrrc8rhOXOSdnMrB7knUC9QlhVePS1mZlZQTgpm5mZFUR7a1+PkbRFLYMxMzOrZ+09Kf8e+GuAdCOKce2UNTMzs25qLymvJ9m/GJKNKIZWPRozM7M61t7o618A35V0d/q+dHWvchERZ+YbmpmZWX1pLyn/E3AJMAMIYCrJHseVBOCkbGZm1g3trei1DDgEQNLbwN9GxH/XKjCzvEybP42mdU251DV68Ohc6jEzqyTr4iG7Am01XZsVWtO6Jp49zisPmeUqzxXCvDrYRpmSckT8QdIASUeSbFLxbuD/gIeAWyPirSrGaGZmRZNnEvXqYBtlSsqStgH+E3g/sBx4GdgP+H/A05I+ERGvVitIMzOzepB1Ra/LgBHAhyNibETsFxFjgQ+lxy+rVoBmZmb1ImtSng6cWT7QKyKeAL4M/E3egZmZmdWbrEl5K2BtG+fWAlvmE46ZmVn9ypqUHwPOlDS49GD6/sz0vJmZmXVD1ilRpwP3AS9I+k+SgV7bANMAkSzDaWZmZt2Q6Uk5Iv4HeA9wHTAK+DhJUr4GeE9EPF21CM3MzOpE1idlImIlcFYVY+mzdjnrjlzq2WH4QB4+68Bc6jIzs+LJnJSt65ZfmM/g9I9euNgJ3sysD3NS7kXyTKJ5Jnhwkjczy4OTcp3KO4H6Kd7MrPuclC0XeSbRPJ/gzcx6kw6TsqStgC8BP/coa6uFHYYPzDUxD9kzt6rMzKqqw6QcEa9LOhv4rxrEY5Z70/VeN3nSgFmh5bkN5K5j8qmnh2Rtvn4c2Ad4oIqxVIWkB4DhJIuc/Ab4XESs6dmorNbc321WYHluA3nTXvnV1QOyJuXZwI8kvQksJFnRK0oLRERLzrHl5dMRsRpA0mXAGcBXezYkq7W8pqW5v9vMqinr2tePA7sBc4D/BdaQbERR+spE0u6SrpX0jKQNku5vo9x4SfdKapHUJOnrkvpnvU6rkoTcDxhM2R8TZmZmRZH1Sflz5JfMJpBsBfkYsEWlApIagHuApcAMkj8ILiX5I+Kczl5Q0kJgX+A5knW8zczMCidTUo6IG3O85oKIuA1A0nxgZIUyJwEDgZlp/+/dkoYCjZIubu0TlvQUUKlX/66IOKYk/unpU/a3gJOBi3P8PlZH8h4Z7j5qMyvVqXnKksYDHwB2Am6IiJck7Q68HBGZmrAj4u0MxQ4mSaylA7LmAhcBk4EFaV37ZI09IjZIugmYh5OydVHeCdR91GZWKlOfsqStJd0M/BK4HjgfGJ2e/iZwbs5xjQOWlR6IiBVAS3ouE0kNkrYtOXQYyXeoVPYESUskLXn11Ve7ELKZmVn3ZH1Svgz4CDAVeBh4reTcQpLFRb6UY1wNwKoKx5vTc52pZ166AIqAXwFfqFQwIq4j2ZqSiRMnejCY1USezeFuCjfr/bIm5ZnAKRFxX4UR0H8Ads43rHxExPMkA7zMCsnLk5pZqaxTogYCf27j3BBgQz7hbNQMVFrepSE9Z2Zm1udkTcpPAJ9p49ws4JF8wtloGWV9x5J2AgZR1tdsZmbWV2Rtvv4qybSke4BbSOYsT5d0KklS/ljOcS0CzpA0pGRU95HAenrhUp9mteD+abPeL+s85YckTQUuBK4kGTR1HskCIAdFxBNZLyhpEMniIQA7AEMlzUrfL0yX67wG+CJwq6SLgLFAI3CZ1602q8z902a9X+Z5yhHxMLC/pIGko6O7uN71NiRP26Va3+8KLI+I5vSPgCtJ5iSvAi4nScxmZmZ9UqcWD0m9BrxJ0pTcaRGxnORJu6NySwG3n5mZWd3IOtALSdMlPUKSlF8CXpP0iKR8tt8xMzOrc5melCWdCFwF3AucArxC0gw9E7hd0skRcW3VojSzmsp7je88eRCa9WVZm6+/AlwbESeXHb9G0jXA2YCTslkfUeSkV9Q/FszykLX5egTw0zbO/QR4dz7hmJmZ1a+sSfk+kt2ZKpkMPJhPOGZmZvWrzebrdJvGVnOA6yWNAH7GO33Kh5Jss3h8NYM0MzOrB+31Kf+SZOWuVgJOTF/BptOa7gTKN6owMzOzTmgvKU+pWRRmZmbWdlKOCK8xbT1i2vxpNK1ryq2+0YNH51aXmVk1dXpFL0kDgC3Lj3dxyU2zzTSta+LZ457t6TDMzGou0+hrScMkXSXpTyQreq2t8DIzM7NuyPqkfCPJ1Kd/BX4LvFGtgMzMzOpV1qQ8FTgxIn5czWDMzMzqWdbFQ1YA7jM2MzOroqxJeTZwjqQx1QzGzMysnmVqvo6IhZIOAn4raTmwqkKZD+Ycm5mZWV3JunXjt4F/AZ7AA73MzMyqIutAr+OBsyPiW9UMxsysI3nv9ez9ma1IsiblFuDJagZiZpZF3gnU+zNbkWQd6PVd4ARJ6rCkmZmZdUnWJ+WRwIeAX0u6n80HekVEnJlnYGZmZvUma1KeBbwFbAF8vMLLTiTBAAAOXklEQVT5AJyUzczMuiHrlKhdqx2ImZlZvcvap2xmZmZVlnWe8skdlYmIq7ofjplZbeU5xcrTq6y7svYpX9nOuUh/OimbWa+TZxL19CrrrkzN1xHRr/wFvBs4GngaGF/NIM3MzOpB1iflzUTEKmCepGHAtcABeQVlZmZWj/IY6PV7YGIO9ZiZmdW1biVlSdsDp5MkZjMzM+uGrKOvX+WdAV2ttgSGAK8BM3OOy8zMrO5k7VP+Hpsn5deAF4E7I+LPuUaVo3T/5xbe2W7y7yJiac9FZGZmVlnWFb0aqxxHtU2PiOU9HYSZ9W3eVtK6q8ujr7tK0u7AGcB+wATgoYg4oEK58cAVablVwPXAeRGxoXbRmpll520lrbvaTMqSFneinoiIqRnLTgCmA4+RbHBR6doNwD3AUmAGsBtwKcnAtHM6EVern6XbTv4caIyIN7tQh5mZWVW196ScpZ94e+AjbN7f3J4FEXEbgKT5JNtCljsJGAjMjIg1wN2ShgKNki5OjyHpKWBMhc/fFRHHpL9PiogXJW0N/AfwJeBbnYjXzMysJtpMyhFxeFvnJI0h2arxU8BK4PKsF4yItzMUO5gksa4pOTYXuAiYDCxI69onw/VeTH/+RdL3gROzxmpmZlZLnZqnLGn3NLH9L/Bp4MvAzhGR95PnOGBZ6YGIWEEyinpc1kokDU6fsJE0ADgMeKaNsidIWiJpyauvvtrlwM3MzLoqU1KWNEHSj4BfAVOAU4DdIuI7EbG+CnE1kAzuKtecnstqW+BBSc+QrNG9AfhGpYIRcV1ETIyIiaNGjepsvGZmZt3W7uhrSR8AziYZbPW/wPHAD3rLCOiIeB74q56Ow8zMLIv2Rl8vAj4BPAscFRG31Cyq5Il4WIXjDek5M7M+z3s915/2npSnpT93BL4n6XvtVRQR2+QWVdKfvEnfsaSdgEGU9TWbmfVV3uu5/rSXlM+rWRSbWwScIWlIRKxNjx0JrAce6LmwzMzMqqe9KVFVScqSBpEsHgKwAzBU0qz0/cKIaAGuAb4I3CrpImAs0AhcVjZNyszMrM+o+TKbwDZAef906/tdgeUR0SxpKnAlyZzkVSRzoRtrFaSZmVmt1TwppxtDKEO5pYBHJZiZWd3o1OIhZmZmVj1OymZmZgXhpGxmZlYQTspmZmYF0ROjr83MrMbyXB2stT6vEJY/J2UzszqQdwL1CmHV4eZrMzOzgnBSNjMzKwgnZTMzs4JwUjYzMysIJ2UzM7OCcFI2MzMrCCdlMzOzgnBSNjMzKwgvHmK5mDZ/Gk3rmnKpa/Tg0bnUY2bW2zgpWy6a1jXx7HHP9nQYZlYjeS7b6SU73+GkbGZmnZZnEvWSne9wn7KZmVlBOCmbmZkVhJOymZlZQTgpm5mZFYSTspmZWUE4KZuZmRWEk7KZmVlBOCmbmZkVhJOymZlZQTgpm5mZFYSTspmZWUE4KZuZmRWEk7KZmVlBeJcoMzPrUXluAzlkz1yq6TF9OilL2g34Scmh7YFHIuLQHgrJzMzK5LkN5F43nZVbXT2hTyfliPgd8Fet7yXdD9zcYwGZmZm1o+Z9ypJ2l3StpGckbUgTZaVy4yXdK6lFUpOkr0vq343r7kySoH/W1TrMzMyqqSeelCcA04HHgC0qFZDUANwDLAVmALsBl5L8EXFOF697DPCTiFjfxc+bmZlVVU8k5QURcRuApPnAyAplTgIGAjMjYg1wt6ShQKOki9NjSHoKGFPh83dFxDFlx44F/jmvL2FmZpa3mifliHg7Q7GDSRLrmpJjc4GLgMnAgrSufbJcU9JEkiT/QOeiNTMzq52iDvQaBywuPRARKyS1pOcWdLK+Y4EfRES0VUDSCcAJ6du/SHoJWN1BvcMylBnJeVqZOdLeZZPvr8+qx2MoaN1dracrn8v6mbzKjQT66v0N1b2/evr6PX1/d+Wzmcqn/xZlKZvt3/B87vGdM5WKiB57AfOB+yscfxP4lwrHXwS+2clrDABeBt7byc9dl1OZJT35v3GV///r8Pv35hjyqrur9XTlc1k/k1e5vnx/53kPFPH6PX1/d+WznSnfW/8NL+qTcm4i4i1g2y58NMvTeGef2PuaInz/asaQV91dracrn8v6mbzL9VU9/f378v3dlc92pnyv/Ddc6V8CPXPxdKBXRBxQdvwV4HsRcV7Z8XVAY0RcUrsou0fSkoiY2NNxmFWD72/r62p9jxd17etlJH3HG0naCRiUnutNruvpAMyqyPe39XU1vceLmpQXAdMkDSk5diSwnl42gjoi/I+W9Vm+v62vq/U9XvM+ZUmDSBYPAdgBGCppVvp+YUS0ANcAXwRulXQRMBZoBC6LTadJmZmZ9Rk171OWtAvw+zZO7xoRy9Ny44Ergf2AVcD1JP3JG6ofpZmZWe316EAvMzMze0dR+5T7PEk7pRtu/ErSc5IultQjq2+YVYukByQ9nW5AMz9dLtesT5H0PUm5POE6Kfect4AzI2JP4K+BDwEzezYks9x9OiL2joj3AyuAM3o6ILM8Sdof2Dqv+pyUOymvrScj4k8RsST9/Q3gGWCnmnwJs3bkub1qRKxOy/YDBgPuL7Melef9LWkr4ELgS3nF1+dX9KqC3LeelDQC+FvgE9UJ2axTcr3HJS0E9gWeA06vWtRm2eR5f38N+H5EvJpX76MHenWSpH6R7nTVzopkXwZmAzvHO9tMziaZ1rVd6bSu9C+tO4GfR8SlNfkSZu3I+x5Pz/UHvgWsjIiLq/4lzNqQ1/0t6f3AZcDHIyIkRUR0OzO7+bqTontbTw4k2XoS2PgP1Q+BXzghW1HkeY+X1LkBuAn4TC5BmnVRjvf3R4HxwO8lLQeQtFzSqO7E56RcHeMoWw40IlYArVtPtroWWIub9Kz36fAel9QgqXQzmMOAX9YsQrOu6/D+joirI2J0ROwSEbukx3aJiFe7c2H3KVdHA8mCJ+Wa03NI+ijwjyT/SP0i7Y+4ISLm1CpIs27o8B5Pf85Lu2gE/Ar4Qm3CM+uWLPd3VTgp95CIeJjkHyqzPikinicZ4GXW5+XRnwxuvq6WZmBYheMN6Tmz3s73uPVlPXZ/OylXR1/aetKsEt/j1pf12P3tpFwdfWbrSbM2+B63vqzH7m/3KXeSt560vs73uPVlRb+/vXhIJ3nrSevrfI9bX1b0+9tJ2czMrCDcp2xmZlYQTspmZmYF4aRsZmZWEE7KZmZmBeGkbGZmVhBOymZmZgXhpGxmZlYQTspmBSapUVK08fr7srIzJf1WUv8K9dyYfub4Nq4Tkg5Ifx8o6RVJ+3cyvp9VOD9Z0m1pfW+mP++QdJSkzP/+SFog6dl2zl8paZWkrSQdUBLTyqzXMCsCJ2Wz4ltNsqpQ+evO1gJpgvs6cEn5ikOS3gUcmr49uqOLRcR64Arg/E7GN7vsuv8C3AdsINlHeSrweWAN8ENgSsb6AX4MvC9dZWkT6R8hs4BbI+J14Kk0nu93on6zQvDa12bF91ZEPNZBmanAbsCPKpybDgwF7gWmSNo+Iv7UQX03AudJ2isi2nxCbSs+SfsA3wa+HhGNZeVvljQHaOmg3lK3peWPBr5adm4KsC1J4iZdm/gxSZ/sRP1mheAnZbO+4TjgPyNibYVzRwN/JHlK7Qcc0VFlEfEC8ATwmS7G8wXgFeCCNup/NCKeLj0m6XhJz0l6XdIfJM0uKb8OWECyU0+5o9JrLe5irGaF4aRs1gtIGlD+KityIPBIhc8NAf4GuDkilpE07W7WhB0Rioj7yw4/AhzUxZA/BiyOiLeyFJZ0BnA18DPgU+nv50v6fEmxHwPvkfSBks9tAcwk+X7eCMN6PSdls+IbAbxZ/kp3u0HSaGB74JcVPvu3wEBgbvr+x8CHJI3NcN2ngb3SPunOGg28UHpAidI/LPqlx4cC5wIXRMTZEXF3RFwIXAScUzJwbRHJbj1HlVQ7DWhIv5dZr+ekbFZ8q4F9K7ya0vPbpT8rjTQ+Gng+Iv47fT8PCDZNbG1ZCfQHRnUtbMq3oDuMTf+wuDg9vh8wGLilrCVgMUlf8Y4AEfEGcCtwhCSlnz0S+APwaBdjNCsUD/QyK763ImJJO+dbn2RfLz0oaSTwceB7koanh9eS9BUfDXyzg+u21teVJ+Um0mRa4l6SPyYAbi85PjL9+Vwbde1EkngheSL+HLCfpKeAGcBV4T1orY9wUjbr/f4v/Tm87Pgskv/GT0lfm5D0voio1OTdqrW+/2unTFseBD4hqX9rX29ENANL0mu/UVK2tf5PAS9XqOvXJb/fl5Y5iqTJfghuurY+xEnZrPdbDrwB7ArcX3L8aOBXwMll5bciGcl8NHB2O/XuAvw5Iv7chZiuAI4FvkLH850fBdYDoyPijvYKRsQGSTcDhwM7AL8qH8Vt1ps5KZsV3wBJH65w/IWI+GNEvCbpSeADwL8BSNoR2B/4coVR1Ui6k+Rps72kPJEKI7qziIinJH0JuEzSX5H0Zf8JGJbGtR3wl7TsKkmNwHcl7UzylN0PeC8wJSIOLav+xyRTrg4lGSBm1mc4KZsV3zAqD2T6Ku/MA74VOKnk3JEkA61+2EadPwDmSfpQRDxefjIdaDUVOL2rQUfEdyT9D3Aa8D2S5vDWJuzPkiTq1rIXS2oCTk2v+Rrwm9IyJWUflbSc5EneTdfWp8jjI8x6P0nbAiuASRHxRA71TQNuJmlSXtdOuUaSRUm2A96OiLe7e+3uSkdm9we+BpwcESM7+IhZYXhKlFkfEBEvA9dTYUBXF50KXN5eQi7ROo/61pyu3V2TSeIpX47TrPDcfG3Wd5wP/GPpiOeukDSQpLn88gzFrwN+nv7e3NVr5uxJ3pl69WZPBmLWWW6+NjMzKwg3X5uZmRWEk7KZmVlBOCmbmZkVhJOymZlZQTgpm5mZFcT/B1mARJjhnDJnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,5))\n",
    "ax = plt.subplot(1,1,1)\n",
    "for coup,en,weight in zip(coups,energies,weights):\n",
    "    if sum(weight)<10**-5 : continue\n",
    "    ax.hist(en, weights=weight, bins=np.logspace(2,4, 20), histtype='step', label=r\"$\\epsilon=$\"+str(coup)) \n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_ylim(10**-7,10**5) \n",
    "    ax.set_xlabel(\"E(A') [GeV]\") \n",
    "    ax.set_ylabel(\"Number of Events per bin\") \n",
    "    ax.legend(frameon=False, labelspacing=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Parameter Scan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we will get the LLP sensitivity reach for different detector configuraions. As an example, we show the FASER2 reach for dark photons for 3 scenarios. We just need to loop over different masses and use the previously introduced funtion `get_events`. The results will be written into a `.npy` file that we can store and plot later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- the nominal/default setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#specify setup\n",
    "if energy == \"14\": luminosity, distance = 3000 , 480\n",
    "if energy == \"27\": luminosity, distance = 15000, 480\n",
    "if energy == \"100\": luminosity, distance = 30000, 1000\n",
    "setup, selection, channels = \"default\", \"np.sqrt(x.x**2 + x.y**2)< 1\", None\n",
    "foresee.set_detector(selection=selection, channels=channels, distance=distance, luminosity=luminosity)\n",
    "\n",
    "#get reach \n",
    "list_nevents = []    \n",
    "for mass in masses:\n",
    "    couplings, _, nevents, _, _ , _ = foresee.get_events(mass=mass, energy=energy)\n",
    "    list_nevents.append(nevents)  \n",
    "    \n",
    "#save results\n",
    "np.save(\"files/models/\"+modelname+\"/results/\"+energy+\"TeV_\"+setup+\".npy\",[masses,couplings,list_nevents])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- a displaced setup, in which the detector is displaced by 1m from it's nominal position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#specify setup\n",
    "setup, selection, channels = \"displaced\", \"np.sqrt((x.x-1)**2 + x.y**2)< 1\", None\n",
    "foresee.set_detector(selection=selection, channels=channels, distance=distance, luminosity=luminosity)\n",
    "\n",
    "#get reach \n",
    "list_nevents = []    \n",
    "for mass in masses:\n",
    "    couplings, _, nevents, _, _ , _ = foresee.get_events(mass=mass, energy=energy)\n",
    "    list_nevents.append(nevents)  \n",
    "    \n",
    "#save results\n",
    "np.save(\"files/models/\"+modelname+\"/results/\"+energy+\"TeV_\"+setup+\".npy\",[masses,couplings,list_nevents])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- a search that only considered dark photon decays into electrons "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#specify setup\n",
    "setup, selection, channels = \"electron\", \"np.sqrt(x.x**2 + x.y**2)< 1\", [\"e_e\"]\n",
    "foresee.set_detector(selection=selection, channels=channels, distance=distance, luminosity=luminosity)\n",
    "\n",
    "#get reach \n",
    "list_nevents = []    \n",
    "for mass in masses:\n",
    "    couplings, _, nevents, _, _ , _ = foresee.get_events(mass=mass, energy=energy)\n",
    "    list_nevents.append(nevents)  \n",
    "    \n",
    "#save results\n",
    "np.save(\"files/models/\"+modelname+\"/results/\"+energy+\"TeV_\"+setup+\".npy\",[masses,couplings,list_nevents])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Plot the Results - Configurations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the results. We first specify all detector setups for which we want to show result (filename in model/results directory, label, color, linestyle, opacity alpha for filled contours, required number of events)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "setups = [\n",
    "    [\"14TeV_default.npy\",    \"default\"  , \"red\",     \"solid\", 0., 3],\n",
    "    [\"14TeV_displaced.npy\",  \"displaced\", \"orange\",  \"solid\", 0., 3],\n",
    "    [\"14TeV_electron.npy\",   \"electron\" , \"magenta\", \"solid\", 0., 3],\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we specify all the existing bounds (filename in model/bounds directory, label, label position x, label position y, label rotation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bounds = [ \n",
    "    [\"bounds_LHCb1.txt\", \"LHCb\",  0.220, 2.8*10**-4, 0  ],\n",
    "    [\"bounds_LHCb2.txt\",  None  , 0    , 0         , 0  ],\n",
    "    [\"bounds_LHCb3.txt\",  None  , 0    , 0         , 0  ],\n",
    "    [\"bounds_E137.txt\",  \"E137\",  0.015, 1.2*10**-7, 0  ],\n",
    "    [\"bounds_CHARM.txt\", \"CHARM\", 0.120, 1.4*10**-7, -5 ],\n",
    "    [\"bounds_NuCal.txt\", \"NuCal\", 0.042, 1.3*10**-5, -28],\n",
    "    [\"bounds_E141.txt\",  \"E141\",  0.011, 5.8*10**-5, 33 ],\n",
    "    [\"bounds_NA64.txt\",  \"NA64\",  0.014, 3.6*10**-4, -35],\n",
    "    [\"bounds_BaBar.txt\", \"BaBar\", 0.360, 1.4*10**-3, 0  ],\n",
    "    [\"bounds_NA48.txt\",  \"NA48\",  0.040, 1.4*10**-3, 0  ],\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then specify other projected sensitivitities (filename in model/bounds directory, color, label, label position x, label position y, label rotation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "projections = [\n",
    "    [\"limits_SeaQuest.txt\",   \"lime\",         \"SeaQuest\", 1.350, 2.1*10**-7, 0  ],\n",
    "    [\"limits_NA62.txt\",       \"limegreen\",    \"NA62\"    , 0.999, 1.1*10**-7, 0  ],\n",
    "    [\"limits_SHiP.txt\",       \"forestgreen\",  \"SHiP\"    , 1.750, 8.2*10**-7, 0  ],\n",
    "    [\"limits_HPS.txt\",        \"deepskyblue\",  \"HPS\"     , 0.050, 1.5*10**-4, 0  ],\n",
    "    [\"limits_HPS-1.txt\",      \"deepskyblue\",  None      , 0    , 0         , 0  ],\n",
    "    [\"limits_Belle2.txt\",     \"blue\",         \"Belle2\"  , 0.570, 1.3*10**-4, 0  ],\n",
    "    [\"limits_LHCb.txt\",       \"dodgerblue\",   \"LHCb\"    , 0.135, 2.8*10**-4, 0  ],\n",
    "    [\"limits_LHCb-mumu1.txt\", \"dodgerblue\",   None      , 0    , 0         , 0  ],\n",
    "    [\"limits_LHCb-mumu2.txt\", \"dodgerblue\",   None      , 0    , 0         , 0  ],\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can plot everything using `foresee.plot_reach()`. It returns a matplotlib instance, to which we can add further lines and which we can show or save. Below, we add the dark matter relict target line for a specific benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = foresee.plot_reach(\n",
    "    setups=setups,\n",
    "    bounds=bounds,\n",
    "    projections=projections,\n",
    "    title=\"Dark Photons\", \n",
    "    xlims = [0.01,3], \n",
    "    ylims=[10**-7,0.002],\n",
    "    xlabel=r\"Dark Photon Mass $m_{A'}$ [GeV]\", \n",
    "    ylabel=r\"Kinetic Mixing $\\epsilon$\",\n",
    "    legendloc=(1,0.68),\n",
    "    figsize=(8,6),\n",
    ")\n",
    "\n",
    "data = foresee.readfile(\"files/models/\"+modelname+\"/lines/scalar_DM_Oh2_intermediate_eps_vs_mAprime.txt\")\n",
    "plot.plot(data.T[0], data.T[1], color=\"k\", lw=2)\n",
    "plot.text(0.010, 3.40*10**-5, \"relic target\",  fontsize=13,color=\"k\",rotation=25)\n",
    "plot.text(0.011, 2.15*10**-5, r\"$m_\\chi\\!=\\!0.6 m_{A'}$\",fontsize=13,color=\"k\",rotation=25)\n",
    "plot.text(0.013, 1.20*10**-5, r\"$\\alpha_D\\!=\\!0.6$\",fontsize=13,color=\"k\",rotation=25)\n",
    "\n",
    "plot.subplots_adjust(left=0.12, right=0.97, bottom=0.10, top=0.95)\n",
    "plot.savefig(\"/Users/felixkling/Downloads/Figure.pdf\")\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "734px",
    "width": "465px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}