MasaZ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<CENTER>\n",
    "    <a href=\"http://opendata.atlas.cern\" class=\"icons\"><img src=\"../Assets/ATLASOD.gif\" style=\"width:50%\"></a>\n",
    "</CENTER>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A more difficult notebook in python\n",
    "\n",
    "### In this notebook you can find a more difficult program that shows further high energy physics (HEP) analysis techniques.\n",
    "\n",
    "##### The following analysis is searching for events where [Z bosons](https://en.wikipedia.org/wiki/W_and_Z_bosons) decay to two leptons of same flavour and opposite charge (to be seen for example in the [Feynman diagram](https://en.wikipedia.org/wiki/Feynman_diagram))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<CENTER><img src=\"../Assets/Z_ElectronPositron.png\" style=\"width:30%\"></CENTER>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all - like we did it in the first notebook - ROOT is imported to read the files in the _.root_ data format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.CodeCell.config_defaults.highlight_modes['magic_text/x-c++src'] = {'reg':[/^%%cpp/]};"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.07/03\n"
     ]
    }
   ],
   "source": [
    "import ROOT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to activate the interactive visualisation of the histogram that is later created we can use the JSROOT magic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##%jsroot on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we have to open the data that we want to analyze. As described above the data is stored in a _*.root_ file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#f = ROOT.TFile.Open(\"http://opendata.atlas.cern/release/samples/MC/mc_147770.Zee.root\")\n",
    "#f = ROOT.TFile.Open(\"/home/student/CEVALE2VE-repos-clases/Input/mc_105986.ZZ.root\")\n",
    "f = ROOT.TFile.Open(\"http://opendata.atlas.cern/release/samples/MC/mc_105986.ZZ.root\")\n",
    "## f = ROOT.TFile.Open(\"http://opendata.atlas.cern/release/samples/MC/mc_147770.Zee.root\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the data is opened we create a canvas on which we can draw a histogram. If we do not have a canvas we cannot see our histogram at the end. Its name is _Canvas_ and its header is _c_. The two following arguments define the width and the height of the canvas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "canvas = ROOT.TCanvas(\"Canvas\",\"c\",800,600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to define a tree named _t_ to get the data out of the _.root_ file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tree = f.Get(\"mini\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define a histogram that will later be placed on this canvas. Its name is _variable_, the header of the histogram is _Mass of the Z boson_, the x axis is named _mass [GeV]_ and the y axis is named _events_. The three following arguments indicate that this histogram contains 30 bins which have a range from 40 to 140."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hist = ROOT.TH1F(\"variable\",\"Mass of the Z boson; mass [GeV]; events\",30,40,140)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to fill our above defined histogram. At first we define some variables and then we loop over the data. We also make some cuts as you can see in the # _comments_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "leadLepton  = ROOT.TLorentzVector()\n",
    "trailLepton = ROOT.TLorentzVector()\n",
    "\n",
    "for event in tree:\n",
    "    \n",
    "    # Cut #1: 2 leptons\n",
    "    if tree.lep_n == 2:\n",
    "        \n",
    "        # Cut #2: Leptons with opposite charge\n",
    "        if (tree.lep_charge[0] != tree.lep_charge[1]):\n",
    "            \n",
    "            # Cut #3: Leptons of the same family (2 electrons or 2 muons)\n",
    "            if (tree.lep_type[0] == tree.lep_type[1]):\n",
    "                \n",
    "                # Let's define one TLorentz vector for each, e.i. two vectors!\n",
    "                leadLepton.SetPtEtaPhiE(tree.lep_pt[0]/1000., tree.lep_eta[0], tree.lep_phi[0], tree.lep_E[0]/1000.)\n",
    "                trailLepton.SetPtEtaPhiE(tree.lep_pt[1]/1000., tree.lep_eta[1], tree.lep_phi[1], tree.lep_E[1]/1000.)\n",
    "                # Next line: addition of two TLorentz vectors above --> ask mass very easy (devide by 1000 to get value in GeV)\n",
    "                invmass = leadLepton + trailLepton\n",
    "                \n",
    "                hist.Fill(invmass.M())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After filling the histogram we want to see the results of the analysis. First we draw the histogram on the canvas and then the canvas on which the histogram lies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hist.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# El número de leptones es “mayor que uno” (distinto de decir “igual a 2”)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c1 = ROOT.TCanvas(\"Canvas1\",\"c\",800,600)\n",
    "h1 = ROOT.TH1F(\"variable1\",\"Mass of the Z boson; mass [GeV]; events\",30,40,140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "leadLepton1  = ROOT.TLorentzVector()\n",
    "trailLepton1 = ROOT.TLorentzVector()\n",
    "\n",
    "for event in tree:\n",
    "    \n",
    "    # Cut #1: At least 2 leptons\n",
    "    if tree.lep_n > 1:\n",
    "        \n",
    "        # Cut #2: Leptons with opposite charge\n",
    "        if (tree.lep_charge[0] != tree.lep_charge[1]):\n",
    "            \n",
    "            # Cut #3: Leptons of the same family (2 electrons or 2 muons)\n",
    "            if (tree.lep_type[0] == tree.lep_type[1]):\n",
    "                \n",
    "                # Let's define one TLorentz vector for each, e.i. two vectors!\n",
    "                leadLepton1.SetPtEtaPhiE(tree.lep_pt[0]/1000., tree.lep_eta[0], tree.lep_phi[0], tree.lep_E[0]/1000.)\n",
    "                trailLepton1.SetPtEtaPhiE(tree.lep_pt[1]/1000., tree.lep_eta[1], tree.lep_phi[1], tree.lep_E[1]/1000.)\n",
    "                # Next line: addition of two TLorentz vectors above --> ask mass very easy (devide by 1000 to get value in GeV)\n",
    "                invmass1 = leadLepton1 + trailLepton1\n",
    "                \n",
    "                h1.Fill(invmass1.M())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h1.Draw()\n",
    "c1.Draw() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cuando el pt del segundo lepton es solo 10% menos que el del leptón más energético (aquel en [0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c2 = ROOT.TCanvas(\"Canvas2\",\"c\",800,600)\n",
    "h2 = ROOT.TH1F(\"variable2\",\"Mass of the Z boson; mass [GeV]; events\",30,40,140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "leadLepton2  = ROOT.TLorentzVector()\n",
    "trailLepton2 = ROOT.TLorentzVector()\n",
    "\n",
    "for event in tree:\n",
    "    \n",
    "    # Cut #1: 2 leptons\n",
    "    if tree.lep_n == 2:\n",
    "        \n",
    "        # Cut #2: Leptons with opposite charge\n",
    "        if (tree.lep_charge[0] != tree.lep_charge[1]):\n",
    "            \n",
    "            # Cut #3: Leptons of the same family (2 electrons or 2 muons)\n",
    "            if (tree.lep_type[0] == tree.lep_type[1]):\n",
    "                \n",
    "                # Cut #4: pt del segundo lepton es solo 10% menos que el del leptón más energético\n",
    "                if (tree.lep_pt[1] > 0.9 * tree.lep_pt[0]):\n",
    "                \n",
    "                    # Let's define one TLorentz vector for each, e.i. two vectors!\n",
    "                    leadLepton2.SetPtEtaPhiE(tree.lep_pt[0]/1000., tree.lep_eta[0], tree.lep_phi[0], tree.lep_E[0]/1000.)\n",
    "                    trailLepton2.SetPtEtaPhiE(tree.lep_pt[1]/1000., tree.lep_eta[1], tree.lep_phi[1], tree.lep_E[1]/1000.)\n",
    "                    # Next line: addition of two TLorentz vectors above --> ask mass very easy (devide by 1000 to get value in GeV)\n",
    "                    invmass2 = leadLepton2 + trailLepton2\n",
    "                \n",
    "                    h2.Fill(invmass2.M())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2.Draw()\n",
    "c2.Draw() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cuando el valor absoluto del ángulo eta de los leptones estudiados es < 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import fabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c3 = ROOT.TCanvas(\"Canvas3\",\"c\",800,600)\n",
    "h3 = ROOT.TH1F(\"variable3\",\"Mass of the Z boson; mass [GeV]; events\",30,40,140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "leadLepton3  = ROOT.TLorentzVector()\n",
    "trailLepton3 = ROOT.TLorentzVector()\n",
    "\n",
    "for event in tree:\n",
    "    \n",
    "    # Cut #1: 2 leptons\n",
    "    if tree.lep_n == 2:\n",
    "        \n",
    "        # Cut #2: Leptons with opposite charge\n",
    "        if (tree.lep_charge[0] != tree.lep_charge[1]):\n",
    "            \n",
    "            # Cut #3: Leptons of the same family (2 electrons or 2 muons)\n",
    "            if (tree.lep_type[1] == tree.lep_type[0]):\n",
    "                \n",
    "                #Cut #4: valor absoluto del ángulo eta de los leptones estudiados es < 2\n",
    "                if (fabs(tree.lep_eta[0]) < 2 and fabs(tree.lep_eta[1]) < 2):\n",
    "                \n",
    "                    # Let's define one TLorentz vector for each, e.i. two vectors!\n",
    "                    leadLepton3.SetPtEtaPhiE(tree.lep_pt[0]/1000., tree.lep_eta[0], tree.lep_phi[0], tree.lep_E[0]/1000.)\n",
    "                    trailLepton3.SetPtEtaPhiE(tree.lep_pt[1]/1000., tree.lep_eta[1], tree.lep_phi[1], tree.lep_E[1]/1000.)\n",
    "                    # Next line: addition of two TLorentz vectors above --> ask mass very easy (devide by 1000 to get value in GeV)\n",
    "                    invmass3 = leadLepton3 + trailLepton3\n",
    "                \n",
    "                    h3.Fill(invmass3.M())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h3.Draw()\n",
    "c3.Draw() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cuando el valor absoluto del ángulo eta de los leptones estudiados es > 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "c4 = ROOT.TCanvas(\"Canvas4\",\"c\",800,600)\n",
    "h4 = ROOT.TH1F(\"variable4\",\"Mass of the Z boson; mass [GeV]; events\",30,40,140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "leadLepton4  = ROOT.TLorentzVector()\n",
    "trailLepton4 = ROOT.TLorentzVector()\n",
    "\n",
    "for event in tree:\n",
    "    \n",
    "    # Cut #1: 2 leptons\n",
    "    if tree.lep_n == 2:\n",
    "        \n",
    "        # Cut #2: Leptons with opposite charge\n",
    "        if (tree.lep_charge[0] != tree.lep_charge[1]):\n",
    "            \n",
    "            # Cut #3: Leptons of the same family (2 electrons or 2 muons)\n",
    "            if (tree.lep_type[1] == tree.lep_type[0]):\n",
    "                \n",
    "                #Cut #4: valor absoluto del ángulo eta de los leptones estudiados es > 2\n",
    "                if (fabs(tree.lep_eta[0]) > 2 and fabs(tree.lep_eta[1]) > 2):\n",
    "                \n",
    "                    # Let's define one TLorentz vector for each, e.i. two vectors!\n",
    "                    leadLepton4.SetPtEtaPhiE(tree.lep_pt[0]/1000., tree.lep_eta[0], tree.lep_phi[0], tree.lep_E[0]/1000.)\n",
    "                    trailLepton4.SetPtEtaPhiE(tree.lep_pt[1]/1000., tree.lep_eta[1], tree.lep_phi[1], tree.lep_E[1]/1000.)\n",
    "                    # Next line: addition of two TLorentz vectors above --> ask mass very easy (devide by 1000 to get value in GeV)\n",
    "                    invmass4 = leadLepton4 + trailLepton4\n",
    "                \n",
    "                    h4.Fill(invmass4.M())\n",
    "                                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h4.Draw()\n",
    "c4.Draw() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ROOT.enableJSVis()\n",
    "#canvas.Draw()\n",
    "#ROOT.disableJSVis() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%%cpp\n",
    "#variable->Fit(\"gaus\", \"S\");\n",
    "#Canvas->Draw();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#scale = hist.Integral()\n",
    "#hist.Scale(1/scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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",
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