Tritium_neutron_fraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling for simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# NEW NUMBERS (11/11)\n",
    "n_sim_dd = 4e9 # Mail from Jacques for new dataset, Nov 11\n",
    "n_at_ne_dd = 116745 # NE213_Combined\n",
    "a_dd = 0.004378590912205715 # From fit, rebinned version (checked)\n",
    "a_dd_rel_unc = 0.00017208925339947404 / 0.004378590912205715\n",
    "# To check if this makes sense between datasets.\n",
    "f_useful_dd = n_at_ne_dd / n_sim_dd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Uncertainty for the low-stat DT dataset\n",
    "# 1.2421 +- 0.233994\n",
    "# print(0.233994 / 1.2421)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DT numbers, Simulation result Nov 1, wrong energy\n",
    "n_sim_dt   = 9.5e8 # Mail from JP Nov 1\n",
    "n_at_ne_dt = 20044 # Dataset from Nov 1; this is at the wrong energy :(\n",
    "f_useful_dt = n_at_ne_dt / n_sim_dt # Fraction of events that is useful\n",
    "# a_dt = 0.043077878353139365 # From fit\n",
    "a_dt = 0.0794423561716775 # THIS IS ACTUALLY FROM THE FIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DT numbers simulation Dec 5., wrong energy again\n",
    "n_sim_dt = 9.5e8 # Mail from JP Dec 5\n",
    "n_at_ne_dt = 14356 # Why so different?\n",
    "f_useful_dt = n_at_ne_dt / n_sim_dt\n",
    "a_dt = 0.08197476716848429 #  +- 0.015573331817021502 according to fit, but may improve with better E\n",
    "a_dt_rel_unc = 0.015573331817021502 / 0.08197476716848429"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of neutrons arriving at NE213: 29.2 per million for DD, 15.1 per million for DT\n"
     ]
    }
   ],
   "source": [
    "# print(f_useful_dd, f_useful_dt)\n",
    "# DD and DT should be more or less the same (right?)\n",
    "print('Fraction of neutrons arriving at NE213: %.1f per million for DD, %.1f per million for DT' %\n",
    "     (f_useful_dd*1e6, f_useful_dt*1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# OK, here goes.\n",
    "# Normalize scaling factor a per number of events.\n",
    "# Does this make sense? Well, suppose you have a giant number of events. Then your scaling factor becomes\n",
    "# smaller and smaller. So, this should be up-corrected, i.e. multiplied by something proportional\n",
    "# to the number of simulated events.\n",
    "#\n",
    "# Other way to look at it: the spectrum should be normalized before fitting, then the scaling factor corrects for\n",
    "# this happening.\n",
    "#\n",
    "# Check: a INcreases if you DEcrease the statistics!\n",
    "a_eff_dd = a_dd * n_sim_dd\n",
    "a_eff_dt = a_dt * n_sim_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Then, there is rebinning. \n",
    "# Lets get this straight. For the high energy, because of rebinning, the event rate is measured per 119 keVee.\n",
    "# So. The factor a you need for dt is actually LOWER since you rebinned to make all values higher.\n",
    "# This will give you a factor 50 or 25, depending on rebinning (discussed later)\n",
    "a_eff_dt = a_eff_dt / 50\n",
    "# \n",
    "# Well, how about checking the non-rebinned factor?\n",
    "# I checked. It is the same. So 50 is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08892818531296637"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The final ratio:\n",
    "(a_eff_dt/a_eff_dd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison to AZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# If we want to check if this result is consistent with Andreas' result, we should apply the same energy selection that \n",
    "# he does. For this, we'll read in the energy and then check what fraction is in range, then correct the fraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_simulation(fn, col_n = 0):    \n",
    "    # Relevant imports to reading in \n",
    "    import ROOT\n",
    "    import root_numpy as rn\n",
    "\n",
    "    # Actually read the root file\n",
    "    f = ROOT.TFile(fn)\n",
    "    treenames = rn.list_trees(fn)\n",
    "    assert len(treenames) == 1\n",
    "    tree = f.Get(treenames[0])\n",
    "    data = rn.tree2array(tree)\n",
    "\n",
    "    # Somehow the data is stored in the form of an array of tuples... wtf? So here's a hack to get it out!\n",
    "    sim_energies = np.array([d[col_n] for d in data])\n",
    "    print('Read a total of %d events from file %s.' % (len(sim_energies), fn))\n",
    "    return sim_energies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read a total of 14356 events from file ./simu/161205_DT/NE213_DT_Combined2.root.\n"
     ]
    }
   ],
   "source": [
    "e_at_ne_dt = read_simulation('./simu/161205_DT/NE213_DT_Combined2.root')\n",
    "\n",
    "# e_at_ne_dt = np.concatenate([\n",
    "#     read_simulation('./simu/161101_14-1_MeV/NE213_DT_Spectrum.root'),\n",
    "#     read_simulation('./simu/161101_14-1_MeV/NE213_DT_Spectrum2.root'),\n",
    "#     read_simulation('./simu/161101_14-1_MeV/NE213_DT_Spectrum3.root'),\n",
    "#     read_simulation('./simu/161101_14-1_MeV/NE213_DT_Spectrum4.root')])/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read a total of 116745 events from file ./simu/161111_DD/NE213_DD_Combined.root.\n"
     ]
    }
   ],
   "source": [
    "e_at_ne_dd = read_simulation('./simu/161111_DD/NE213_DD_Combined.root')/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This is the number of neutron events in the specified energy range\n",
    "n_in_range_dd = len(e_at_ne_dd[(e_at_ne_dd > 2.0) & (e_at_ne_dd < 2.8) ])\n",
    "n_in_range_dt = len(e_at_ne_dt[(e_at_ne_dt > 2.5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ...And this is the fraction of events in range\n",
    "f_in_range_dt = n_in_range_dt / len(e_at_ne_dt)\n",
    "f_in_range_dd = n_in_range_dd / len(e_at_ne_dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6242494325238769 0.9621760936193926\n"
     ]
    }
   ],
   "source": [
    "# By chance, these are nearly the same, so the correction is small.\n",
    "print(f_in_range_dd, f_in_range_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13706792429293665"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now correction the previously found ratio\n",
    "# It makes sense that fraction is LOWER, since a lower number of DT neutrons are in the energy range.\n",
    "((a_eff_dt * f_in_range_dt) / (a_eff_dd * f_in_range_dd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Compare the two HE datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read a total of 3808 events from file ./simu/161101_14-1_MeV/NE213_DT_Spectrum.root.\n",
      "Read a total of 3871 events from file ./simu/161101_14-1_MeV/NE213_DT_Spectrum2.root.\n",
      "Read a total of 6103 events from file ./simu/161101_14-1_MeV/NE213_DT_Spectrum3.root.\n",
      "Read a total of 6262 events from file ./simu/161101_14-1_MeV/NE213_DT_Spectrum4.root.\n",
      "Read a total of 14356 events from file ./simu/161205_DT/NE213_DT_Combined2.root.\n"
     ]
    }
   ],
   "source": [
    "e_at_ne_dt_nov01 = np.concatenate([\n",
    "    read_simulation('./simu/161101_14-1_MeV/NE213_DT_Spectrum.root'),\n",
    "    read_simulation('./simu/161101_14-1_MeV/NE213_DT_Spectrum2.root'),\n",
    "    read_simulation('./simu/161101_14-1_MeV/NE213_DT_Spectrum3.root'),\n",
    "    read_simulation('./simu/161101_14-1_MeV/NE213_DT_Spectrum4.root')])/1000\n",
    "e_at_ne_dt_dec05 = read_simulation('./simu/161205_DT/NE213_DT_Combined2.root')/1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAG0CAYAAACCDgu9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wf2WAhA8ivTcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddda3aa128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10,7))\n",
    "plt.hist(e_at_ne_dt_dec05, range=(0,15), bins=150, histtype= 'step')\n",
    "plt.hist(e_at_ne_dt_nov01, range=(0,15), bins=150, histtype= 'step')\n",
    "plt.ylabel('Counts per 100 keV')\n",
    "plt.xlabel('Energy (MeV)')\n",
    "plt.savefig('dataset_comparison.png', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "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",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.5"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}