Week6/original/ATLAStestbeam/ATLAStestbeam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ATLAS Test Beam Data\n",
    "\n",
    "Python script for analysis of ATLAS test beam data. \n",
    "\n",
    "The program reads an ASCII (i.e. text) data file, containting a large number of events, where a charged particle (electron or pion) passed through a slice of the ATLAS detector. Each passage is recorded by different detectors (essentially three independent ones!), boiling down to eleven numbers (some more relevant than others). The exercise is to separate electron and pion events based on these numbers, and in turn use this information to measure the interaction of pions and electrons seperately.\n",
    "\n",
    "NOTE: Though the data is from particle physics, it could in principle have been from ANY other source, and the eleven numbers could for example have been indicators of cancer, key numbers for investors, or index numbers for identifying potential costumors.\n",
    "\n",
    "For more information on ATLAS test beam: http://www.nbi.dk/~petersen/Teaching/Stat2020/TestBeam/TestBeamDataAnalysis.html.\n",
    "\n",
    "***\n",
    "\n",
    "### Authors: \n",
    "- Troels C. Petersen (Niels Bohr Institute)\n",
    "- Christian Michelsen (Niels Bohr Institute)\n",
    "\n",
    "### Date:    \n",
    "- 02-01-2020 (latest update)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set parameters of the program:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write extensive output\n",
    "verbose = True\n",
    "N_verbose = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open file, `DataSet_AtlasPid_ElectronPion_2GeV.txt`, and read in all the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cher:  634.0    nLT, nHT: 32,  8    EM:  0.12  0.47  0.87 -0.04   Had:  0.00  0.00  0.00    Muon: 417.0\n",
      "Cher:  816.0    nLT, nHT: 42, 12    EM:  0.05  0.87  0.77 -0.01   Had:  0.00  0.00  0.00    Muon: 388.0\n",
      "Cher:  943.0    nLT, nHT: 46,  8    EM:  0.19  0.53  0.96  0.01   Had:  0.00  0.00  0.00    Muon: 377.0\n",
      "Cher:  907.0    nLT, nHT: 33,  3    EM:  0.20  0.74  0.72  0.02   Had:  0.00  0.00  0.00    Muon: 372.0\n",
      "Cher:  775.0    nLT, nHT: 35,  4    EM: -0.01  0.82  0.57  0.02   Had:  0.00  0.00  0.00    Muon: 392.0\n",
      "Cher:  773.0    nLT, nHT: 39,  9    EM:  0.10  1.24  0.27 -0.03   Had:  0.00  0.00  0.00    Muon: 398.0\n",
      "Cher:  782.0    nLT, nHT: 30,  7    EM:  0.35  0.89  0.60 -0.04   Had:  0.00  0.00  0.00    Muon: 369.0\n",
      "Cher:  700.0    nLT, nHT: 39,  9    EM:  0.03  1.14  0.60  0.02   Had:  0.00  0.03  0.76    Muon: 408.0\n",
      "Cher:  542.0    nLT, nHT: 42,  1    EM: -0.03 -0.01  0.13 -0.05   Had:  0.00  0.00  0.00    Muon: 412.0\n",
      "Cher:  752.0    nLT, nHT: 40,  7    EM:  0.09  0.66  0.57  0.10   Had:  0.00  0.00  0.00    Muon: 387.0\n",
      "Cher:  576.0    nLT, nHT: 29,  1    EM:  0.08  0.02  0.04  0.03   Had:  0.00  0.00  0.00    Muon: 392.0\n",
      "Cher:  772.0    nLT, nHT: 46,  6    EM:  0.15  1.11  0.49  0.09   Had:  0.03  0.00  0.00    Muon: 393.0\n",
      "Cher:  735.0    nLT, nHT: 39,  8    EM: -0.02  0.72  1.04  0.09   Had:  0.00  0.00  0.00    Muon: 413.0\n",
      "Cher:  751.0    nLT, nHT: 44,  7    EM:  0.43  1.11  0.33  0.03   Had:  0.00  0.00  0.00    Muon: 359.0\n",
      "Cher:  903.0    nLT, nHT: 37,  6    EM:  0.26  0.58  0.67 -0.04   Had:  0.00  0.00  0.00    Muon: 394.0\n",
      "Cher:  557.0    nLT, nHT: 44,  3    EM: -0.06 -0.01  0.38 -0.04   Had:  0.30  1.32  0.81    Muon: 505.0\n",
      "Cher:  635.0    nLT, nHT: 45,  7    EM:  0.08 -0.07  0.04  0.01   Had:  0.00  0.00  0.00    Muon: 378.0\n",
      "Cher:  801.0    nLT, nHT: 36,  3    EM:  0.45  0.93  0.47 -0.00   Had:  0.00  0.00  0.00    Muon: 425.0\n",
      "Cher:  940.0    nLT, nHT: 27,  3    EM:  0.27  1.05  0.56 -0.10   Had:  0.00  0.00  0.00    Muon: 373.0\n",
      "Cher:  760.0    nLT, nHT: 39, 10    EM:  0.23  1.11  0.32 -0.04   Had:  0.00  0.00  0.00    Muon: 421.0\n"
     ]
    }
   ],
   "source": [
    "data = np.loadtxt('DataSet_AtlasPid_ElectronPion_2GeV.txt', skiprows=1, unpack=True)\n",
    "Cher, nLT, nHT, EM0, EM1, EM2, EM3, Had0, Had1, Had2, Muon = data\n",
    "Cher.shape\n",
    "\n",
    "if (verbose) :\n",
    "    for i in range (N_verbose) :\n",
    "        print(f\"Cher: {Cher[i]:6.1f}    nLT, nHT: {int(nLT[i]):2d}, {int(nHT[i]):2d}    EM: {EM0[i]:5.2f} {EM1[i]:5.2f} {EM2[i]:5.2f} {EM3[i]:5.2f}   Had: {Had0[i]:5.2f} {Had1[i]:5.2f} {Had2[i]:5.2f}    Muon: {Muon[i]:5.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your analysis:\n",
    "\n",
    "This is where your analysis should go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "# This is where your analysis should go:\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot the results, cuts or whatever type of analysis choices you have made:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(16, 10))\n",
    "\n",
    "ax[0,0].hist(Cher, bins=200, range=(400, 1400), histtype='step', label='Cherenkov')\n",
    "ax[0,0].set(xlabel=\"Cherenkov\", ylabel=\"Frequency\")\n",
    "\n",
    "ax[1,1].hist(EM1,  bins=200, range=(-0.5, 2.0), histtype='step', label='EM1')\n",
    "ax[1,1].set(xlabel=\"EM1\", ylabel=\"Frequency\")\n",
    "\n",
    "#                 (binx, biny, (x1,  x2),   (  y1,  y2)) or \"binary\"\n",
    "h = ax[0,1].hist2d(EM1,  Cher, (100, 100), ((-0.5, 2.0), (400, 1400)), cmap=\"Reds\")\n",
    "plt.colorbar(h[3], ax=ax[0, 1])                    # z-scale on the right of the figure\n",
    "ax[0,1].set(xlabel=\"EM1\", ylabel=\"Cherenkov\")\n",
    "\n",
    "# You can add your own fourth plot:\n",
    "fig.delaxes(ax[1,0])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: The simple plotting function gives a warning for EM1 (not Cher). Ask yourself, if you did a check of the original data file input values in any way? That is always \"healthy\"!\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we show a simple, quick Seaborn function `jointplot` and how it can be very useful when visualizing 2D-distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f82a68437d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the joint distribution using kernel density estimation\n",
    "g = sns.jointplot(Cher, EM1, xlim=(400, 1400), ylim=(-0.5, 2.0), kind=\"kde\", height=10, space=0) # also try kind=\"hex\" or \"kde\"\n",
    "g.set_axis_labels(xlabel='Cher', ylabel='EM1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions to be answered:\n",
    "\n",
    "Generally, this analysis is about separating electrons and pions (and determining how well this can be done), followed by a few questions characterizing the detector response to each type of particle. Thus, you should imagine, that your new detector/equipment/questionaire gave you this output, and now it is up to you to find out, what this tells you about your experiment, and how to extract information from it in the best possible way.\n",
    "\n",
    "Below are questions guiding you, some/most of which your analysis should cover, but you do **not** have to follow them blindly (I've put \"Optional\" on those that are not essential). \n",
    "\n",
    "Start by considering the data, and get a feel for the typical range of each variable. Plot the variables, both in 1D and also 2D! From considering these plots, guess/estimate an approximate knowledge of how electrons and pions distribute themselves in the variables above, and how to make a selection of these.\n",
    "\n",
    "As described on the webpage introducing the data, there are three (relevant) detectors:\n",
    "- Cherenkov,\n",
    "- TRT (Transition Radiation Tracker) and\n",
    "- Calorimeters\n",
    "\n",
    "They are each capable of separating electrons and pions. As they are _INDEPENDENT_ (three separate detectors), they may be used to cross check each other, and this is what you should use, in fact the essential part of this (and many other) analysis!\n",
    "\n",
    "\n",
    "Questions:\n",
    "----------\n",
    "1. Find for each of these three detector systems one variable (i.e. a combination), which seem to separate electrons and pions best. For example, start with the Cherenkov, which is only a single number, and assume/guess that the large peak at low values is mainly from pions, while the upper broad peak is from electrons (this you would know, as you designed the experiment).  Now plot the TRT and Calorimeter distributions when the Cherenkov selects a pion and afterwards an electron. This should give you a good idea about how to separate pions and electrons using the TRT and Calorimeters.\n",
    "\n",
    "    Hint: Sometimes variables from a single detector are more powerful, when they are combined, e.g. taken ratios of. For the TRT this may be somewhat doable, but for the EMcalo, it is not as simple. Here, one variable caries most of the separation power, but involving other layers in a ratio may enhance the separation power.\n",
    "\n",
    "\n",
    "2. Next you should try to see, if you can make a selection, which gives you a fairly large and clean electron and pion sample, respectively. The question is, how can you know how clean your sample is and how efficient your selection is?  This can actually be measured in the data itself, using the fact that there are three independent detectors. For example, start by making an electron and a pion selection using two of the three variables, and plot the third variable for each of these selections. Now you can directly see, how electrons and pions will distribute themselves in this third variable. Are you worried, that there are pions in your electron sample, and vice versa? Well, there will probably be, but so few, that it won't matter too much, at least not to begin with. Why? Well, let us assume that for each detector, 80% of electrons pass your requirement, but also 10% of pions do. Assuming an even number of electrons and pions (which is not really the case), then with two detector cuts, you should get a sample, which is: $\\frac{0.8\\cdot0.8} {0.8\\cdot0.8 + 0.1\\cdot0.1} = 98.5\\%$ pure.\n",
    "\n",
    "    Now with this sample based on cuts on the two other detectors, ask what fraction of electrons and pions passes your electron selection. The fraction of electrons, that are not selected as electrons will be your TYPE I errors, denoted alpha, while the fraction of pions, that do pass the cut will be your TYPE II errors, denoted beta. Measure these for each of the two cuts in the three detector types, and ask yourself if they are \"reasonable\", i.e. something like in the example above. If not, then you should perhaps reconsider adjusting your cuts.\n",
    "\n",
    "    By now, you should for each detector have 6 numbers:\n",
    "    - The electron cut value above which you accept an electron.\n",
    "    - The efficiency (i.e. 1-alpha) for electrons of this cut.\n",
    "    - The fake rate (i.e. beta) for pions of this cut.\n",
    "    - The pion cut value below which you accept a pion (may be same value as above for electrons!).\n",
    "    - The efficiency (i.e. 1-alpha) for pions of this cut.\n",
    "    - The fake rate (i.e. beta) for electrons of this cut.\n",
    "\n",
    "\n",
    "3. Given the efficiencies and fake rates of each cut, try to combine these (again assuming that they are independent) into knowledge of your sample purities and also the total number of electrons and pions in the whole sample. Do the sum of estimated electrons and pions added actually match the number of particles in total? This is a good cross check!\n",
    "\n",
    "\n",
    "4. If the number of pions was suddenly 1000 times that of elections, would you still be able to get a sample of fairly pure (say 90% pure) electrons? And if so, what would the efficiency for these electrons be? That is equivalent of asking, if you can get a 99.9% pure electron sample from the data given.\n",
    "\n",
    "\n",
    "5. Expanding on problem 2), try now to calculate ROC curves for each of the three detectors. These are obtained by making a clean selection using the two other detectors for electrons and pions seperately, and then integrating over these two distributions, using the running (normalised) integral of each as x and y coordinate in a (ROC) graph. If you do not manage on your own, perhaps consider the ROC calculator example, which is posted along with this exercise.\n",
    "\n",
    "\n",
    "6. __(Partially optional)__: One of the purposes of the testbeam was to measure the response of the TRT detector to exactly electrons and pions. Consider for example only events that has 33 TRT hits (i.e. `nLT` $= 33$). As the High-Threshold probability (i.e. probability of passing the High-Threshold, given that the Low-Threshold was passed), is assumed to be constant in the TRT detector (but quite different for electrons and pions), what distribution should the number of High-Threshold hits (`nHT`) follow? And is that really the case, both for electrons and pions?\n",
    "\n",
    "\n",
    "7. __(Optional)__: Still considering `nLT` $= 33$, and given that there are both electrons and pions in the sample (each with a different HT probability), `nHT` should in the unselected data be a combination of two distributions. Try to fit the number of HT hits with two distributions combined. Do they fit the data? And can this fit be used to estimate the fraction of pions and electrons in the sample? And does that estimate match you previous estimate? Perhaps retry with other values for the number of TRT hits.\n",
    "\n",
    "\n",
    "8. __(Optional)__: Try to select pions using three different (mutually exclusive) techniques:\n",
    "    1. Passing only a hadronic calorimeter requirement (e.g. that the sum of the three HCal values is above some minimum energy).\n",
    "    2. Passing only Cherenkov AND EMcalo requirements.\n",
    "    3.  Passing both A) and B).\n",
    "\n",
    "Try to measure the HT probability (i.e. fraction of High-Threshold hits) for each of these three pion samples. Do they agree with each other?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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