D0AndPtResolution.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "RSA Muons Resolutions for $d_{0}$ and $p_{T}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We compute resolutions for RSA muons passing the selection:\n",
      "- $p_T > 26$ GeV/c,\n",
      "- $|\\eta| < 2$,\n",
      "- $\\chi^2/ndof < 2$,\n",
      "- At least three chambers with valid hits,\n",
      "- At least 17 hits (DT+CSC+RPC),\n",
      "- $d_0/\\sigma > 4$.\n",
      "\n",
      "We find that for RSA muons passing this selection the $d_0$ resolution is $\\sim 2$cm and the relative $p_T$ resolution is $\\sim$ 40%. The $d_0$ measured with RSA muons is found to be in good agreement with the MC-truth values. The $p_T$ is found to be biased and with long non-gaussian tails.\n",
      "\n",
      "The sample used for this study is a signal MC with $H\\rightarrow XX$, where $H$ is a spin 0 boson and $X$ is a long-lived spin 0 boson decaying to a pair of muons. Events are generated with three different lifetimes, and allow to cover a wide range of $d_0$ and $p_T$ values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import ROOT\n",
      "from ROOT import TCanvas\n",
      "import rootprint\n",
      "import rootnotes\n",
      "from array import array"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ROOT.gStyle.SetOptStat(0000000)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from RefittedStandAloneMuons import *\n",
      "from ProgressBar import ProgressBar"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inputData = ROOT.TFile(\"/home/demattia/cmslpc/eos/uscms/store/user/lpcdve/noreplica/DisplacedLeptons/Trees/HTo2LongLivedTo4F_MH1000_MFF350_CTau350To35000_analysis_20140805/histograms.root\")\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TClass::TClass:0: RuntimeWarning: no dictionary for class Candidates is available\n",
        "TClass::TClass:0: RuntimeWarning: no dictionary for class TreeLepton is available\n",
        "TClass::TClass:0: RuntimeWarning: no dictionary for class MuonTime is available\n",
        "TClass::TClass:0: RuntimeWarning: no dictionary for class TreeDipseudoLeptonCandidate is available\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ROOT.gROOT.LoadMacro(\"interfaceRefittedStandAloneMuons/Loader.C+\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "directory = inputData.Get(\"muTrackAnalysis\")\n",
      "tree = directory.Get(\"outputTree\")\n",
      "\n",
      "p = ProgressBar(tree.GetEntries())\n",
      "\n",
      "genD0 = ROOT.TH1F(\"genD0\", \"generated d_{0}\", 250, 0, 250)\n",
      "recoD0 = ROOT.TH1F(\"recoD0\", \"reconstructed d_{0}\", 250, 0, 250)\n",
      "deltaD0 = ROOT.TH1F(\"deltaD0\", \"reconstructed d_{0} - generated d_{0}\", 1000, -10, 10)\n",
      "# deltaD0_0_3 = ROOT.TH1F(\"deltaD0_0_3\", \"reconstructed d_{0} - generated d_{0} (0-3)cm\", 1000, -10, 10)\n",
      "deltaD0_0_10 = ROOT.TH1F(\"deltaD0_0_10\", \"reconstructed d_{0} - generated d_{0} (0-10)cm\", 1000, -10, 10)\n",
      "deltaD0_10_25 = ROOT.TH1F(\"deltaD0_10_25\", \"reconstructed d_{0} - generated d_{0} (10-25)cm\", 1000, -10, 10)\n",
      "deltaD0_25_55 = ROOT.TH1F(\"deltaD0_25_55\", \"reconstructed d_{0} - generated d_{0} (25-55)cm\", 1000, -10, 10)\n",
      "deltaD0_55_110 = ROOT.TH1F(\"deltaD0_55_110\", \"reconstructed d_{0} - generated d_{0} (55-110)cm\", 1000, -10, 10)\n",
      "deltaD0_110 = ROOT.TH1F(\"deltaD0_110\", \"reconstructed d_{0} - generated d_{0} (>110)cm\", 1000, -10, 10)\n",
      "\n",
      "genPt = ROOT.TH1F(\"genPt\", \"generated pt_{0}\", 250, 0, 1000)\n",
      "recoPt = ROOT.TH1F(\"recoPt\", \"reconstructed pt_{0}\", 250, 0, 1000)\n",
      "deltaPtOverPt = ROOT.TH1F(\"deltaPtOverPt\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T}\", 1000, -2, 10)\n",
      "deltaPtOverPt_0_10 = ROOT.TH1F(\"deltaPtOverPt_0_10\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (0-10)cm\", 1000, -2, 10)\n",
      "deltaPtOverPt_10_25 = ROOT.TH1F(\"deltaPtOverPt_10_25\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (10-25)cm\", 1000, -2, 10)\n",
      "deltaPtOverPt_25_55 = ROOT.TH1F(\"deltaPtOverPt_25_55\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (25-55)cm\", 1000, -2, 10)\n",
      "deltaPtOverPt_55_110 = ROOT.TH1F(\"deltaPtOverPt_55_110\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (55-110)cm\", 1000, -2, 10)\n",
      "deltaPtOverPt_110 = ROOT.TH1F(\"deltaPtOverPt_110\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (>110)cm\", 1000, -2, 10)\n",
      "\n",
      "deltaPtOverPt_pt_0_80 = ROOT.TH1F(\"deltaPtOverPt_pt_0_80\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (0-80)GeV/c\", 1000, -2, 10)\n",
      "deltaPtOverPt_pt_80_130 = ROOT.TH1F(\"deltaPtOverPt_pt_80_130\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (80-130)GeV/c\", 1000, -2, 10)\n",
      "deltaPtOverPt_pt_130_180 = ROOT.TH1F(\"deltaPtOverPt_pt_130_180\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (130-180)GeV/c\", 1000, -2, 10)\n",
      "deltaPtOverPt_pt_180_230 = ROOT.TH1F(\"deltaPtOverPt_pt_180_230\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (180-230)GeV/c\", 1000, -2, 10)\n",
      "deltaPtOverPt_pt_230_300 = ROOT.TH1F(\"deltaPtOverPt_pt_230_300\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (230-300)GeV/c\", 1000, -2, 10)\n",
      "deltaPtOverPt_pt_300 = ROOT.TH1F(\"deltaPtOverPt_110\", \"(reconstructed p_{T} - generated p_{T})/generated p_{T} (>300)GeV/c\", 1000, -2, 10)\n",
      "\n",
      "num = 0\n",
      "for event in tree:\n",
      "    if num%(event.GetEntries()/100) == 0:\n",
      "        p.animate(num)\n",
      "    \n",
      "    num += 1\n",
      "    \n",
      "    for lepton in event.candidates.leptons_:\n",
      "        if not (lepton.isStandAloneMuon and lepton.pt > 26 and abs(lepton.eta) < 2 and \\\n",
      "                lepton.dtStationsWithValidHits+lepton.cscStationsWithValidHits > 2 and \\\n",
      "                lepton.trackChi2 < 2 and lepton.numberOfHits > 17 and abs(lepton.genD0) > 0 and lepton.genPt > 0):\n",
      "            continue\n",
      "        if not lepton.d0Significance_PV_includingPVError > 4:\n",
      "            continue\n",
      "        # print lepton.d0_PV, lepton.d0_PV/lepton.d0Significance_PV_includingPVError, lepton.d0Significance_PV_includingPVError, abs(lepton.genD0)\n",
      "        genPt.Fill(lepton.genPt)\n",
      "        recoPt.Fill(lepton.pt)\n",
      "        ptDiff = (lepton.pt - lepton.genPt)\n",
      "        ptTerm = ptDiff/lepton.genPt\n",
      "        deltaPtOverPt.Fill(ptTerm)\n",
      "        genD0.Fill(abs(lepton.genD0))\n",
      "        recoD0.Fill(abs(lepton.d0_PV))\n",
      "        deltaD0.Fill(lepton.d0_PV - abs(lepton.genD0))\n",
      "        if abs(lepton.genD0) < 10:\n",
      "            deltaD0_0_10.Fill(lepton.d0_PV - abs(lepton.genD0))\n",
      "            deltaPtOverPt_0_10.Fill(ptTerm)\n",
      "        elif abs(lepton.genD0) < 25:\n",
      "            deltaD0_10_25.Fill(lepton.d0_PV - abs(lepton.genD0))\n",
      "            deltaPtOverPt_10_25.Fill(ptTerm)\n",
      "        elif abs(lepton.genD0) < 55:\n",
      "            deltaD0_25_55.Fill(lepton.d0_PV - abs(lepton.genD0))\n",
      "            deltaPtOverPt_25_55.Fill(ptTerm)\n",
      "        elif abs(lepton.genD0) < 110:\n",
      "            deltaD0_55_110.Fill(lepton.d0_PV - abs(lepton.genD0))\n",
      "            deltaPtOverPt_55_110.Fill(ptTerm)\n",
      "        else:\n",
      "            deltaD0_110.Fill(lepton.d0_PV - abs(lepton.genD0))\n",
      "            deltaPtOverPt_110.Fill(ptTerm)\n",
      "\n",
      "        if lepton.genPt < 80:\n",
      "            deltaPtOverPt_pt_0_80.Fill(ptTerm)\n",
      "        elif lepton.genPt < 130:\n",
      "            deltaPtOverPt_pt_80_130.Fill(ptTerm)\n",
      "        elif lepton.genPt < 180:\n",
      "            deltaPtOverPt_pt_130_180.Fill(ptTerm)\n",
      "        elif lepton.genPt < 230:\n",
      "            deltaPtOverPt_pt_180_230.Fill(ptTerm)\n",
      "        elif lepton.genPt < 300:\n",
      "            deltaPtOverPt_pt_230_300.Fill(ptTerm)\n",
      "        else:\n",
      "            deltaPtOverPt_pt_300.Fill(ptTerm)\n",
      "\n",
      "    # if num > 100000:\n",
      "    #     break\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************62%*****                  ]  281295 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************63%*****                  ]  285832 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************64%******                 ]  290369 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************65%******                 ]  294906 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************66%*******                ]  299443 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************67%*******                ]  303980 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************68%********               ]  308517 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************69%********               ]  313054 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************70%*********              ]  317591 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************71%*********              ]  322128 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************72%**********             ]  326665 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************73%**********             ]  331202 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************74%***********            ]  335739 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************75%***********            ]  340276 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************76%***********            ]  344813 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************77%************           ]  349350 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************78%************           ]  353887 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************79%*************          ]  358424 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************80%*************          ]  362961 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************81%**************         ]  367498 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************82%**************         ]  372035 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************83%***************        ]  376572 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************84%***************        ]  381109 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************86%****************       ]  390183 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************87%*****************      ]  394720 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************88%*****************      ]  399257 of 453701 complete"
       ]
      },
      {
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       "stream": "stdout",
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        "\r",
        " [**********************89%******************     ]  403794 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************90%******************     ]  408331 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************91%*******************    ]  412868 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************93%********************   ]  421942 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************94%********************   ]  426479 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************95%*********************  ]  431016 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\r",
        " [**********************96%*********************  ]  435553 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************97%********************** ]  440090 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************98%********************** ]  444627 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [**********************99%***********************]  449164 of 453701 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: genD0 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: recoD0 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaD0 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaD0_0_10 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaD0_10_25 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaD0_25_55 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaD0_55_110 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaD0_110 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: genPt (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: recoPt (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_0_10 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_10_25 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_25_55 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_55_110 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_110 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_pt_0_80 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_pt_80_130 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_pt_130_180 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_pt_180_230 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_pt_230_300 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: deltaPtOverPt_110 (Potential memory leak).\n"
       ]
      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def setPlotStyle(genD0, xTitle):\n",
      "    genD0.SetLineColor(1)\n",
      "    genD0.SetTitle(\"\")\n",
      "    genD0.GetXaxis().SetTitle(xTitle)\n",
      "    genD0.GetXaxis().SetTitleOffset(1.3)\n",
      "    genD0.GetYaxis().SetTitle(\"Entries\")\n",
      "    genD0.GetYaxis().SetTitleOffset(1.4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = ROOT.TCanvas(\"c\", \"c\", 400, 400)\n",
      "#c.Divide(2,2)\n",
      "#c.cd(1)\n",
      "setPlotStyle(genD0, \"d_{0} [cm]\")\n",
      "genD0.GetYaxis().SetTitleOffset(1.5)\n",
      "#genD0.Draw()\n",
      "#c.cd(2)\n",
      "setPlotStyle(recoD0, \"d_{0} [cm]\")\n",
      "#recoD0.Draw()\n",
      "#c.cd(3)\n",
      "genD0.Draw(\"E\")\n",
      "genD0.SetMarkerStyle(22)\n",
      "genD0.SetMarkerSize(0.7)\n",
      "genD0.SetMarkerColor(2)\n",
      "genD0.SetLineColor(2)\n",
      "recoD0.Draw(\"sameE\")\n",
      "recoD0.SetMarkerStyle(24)\n",
      "recoD0.SetMarkerSize(0.7)\n",
      "leg = ROOT.TLegend(0.4,0.6,0.8,0.8)\n",
      "leg.SetBorderSize(0)\n",
      "leg.SetFillColor(0)\n",
      "leg.AddEntry(genD0,\"Generated d_{0}\",\"pl\")\n",
      "leg.AddEntry(recoD0,\"Reconstructed d_{0}\",\"pl\")\n",
      "leg.Draw(\"same\");\n",
      "c.SaveAs(\"D0Distributions.C\")\n",
      "c.SaveAs(\"D0Distributions.root\")\n",
      "c.SaveAs(\"D0Distributions.pdf\")\n",
      "c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: c\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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oyeVyS/X6Qqv1Wjr9GnAfAGADT3GljxCXsTP34rPBGLLGZ5rpdPoq8Msvvnjs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\nKFKEkFhDnxQhJNZQpAghsYYiRQiJNf8f252+OITVHMEAAAAASUVORK5CYII=\n",
       "prompt_number": 175,
       "text": [
        "<ROOT.TCanvas object (\"c\") at 0x4330660>"
       ]
      }
     ],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%rootprint\n",
      "deltaD0_0_10.Fit(\"gaus\")\n",
      "deltaD0_10_25.Fit(\"gaus\")\n",
      "deltaD0_25_55.Fit(\"gaus\")\n",
      "deltaD0_55_110.Fit(\"gaus\")\n",
      "deltaD0_110.Fit(\"gaus\")\n",
      "deltaD0.Fit(\"gaus\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " FCN=1221.22 FROM MIGRAD    STATUS=CONVERGED      76 CALLS          77 TOTAL\n",
        "                     EDM=3.62548e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     7.97246e+01   9.54944e-01   1.18490e-02   3.56328e-04\n",
        "   2  Mean         4.26489e-01   1.23881e-02   1.97135e-04  -4.32622e-02\n",
        "   3  Sigma        1.32168e+00   1.09420e-02   2.82455e-05  -9.11917e-02\n",
        " FCN=1641.37 FROM MIGRAD    STATUS=CONVERGED      76 CALLS          77 TOTAL\n",
        "                     EDM=8.117e-09    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.09825e+02   1.01204e+00   1.41327e-02  -5.48441e-05\n",
        "   2  Mean         5.01612e-02   1.12554e-02   2.21320e-04   1.68393e-03\n",
        "   3  Sigma        1.71810e+00   1.12351e-02   2.73700e-05   3.98348e-02\n",
        " FCN=1851.58 FROM MIGRAD    STATUS=CONVERGED      80 CALLS          81 TOTAL\n",
        "                     EDM=2.6327e-10    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     8.19790e+01   8.52747e-01   1.21970e-02  -3.35687e-05\n",
        "   2  Mean        -3.88443e-02   1.37877e-02   2.89336e-04  -8.70666e-04\n",
        "   3  Sigma        1.94246e+00   1.48331e-02   3.22053e-05  -1.04108e-02\n",
        " FCN=1537.41 FROM MIGRAD    STATUS=CONVERGED      79 CALLS          80 TOTAL\n",
        "                     EDM=2.74726e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     5.28915e+01   6.99947e-01   9.10305e-03  -2.67409e-05\n",
        "   2  Mean        -3.91935e-02   1.68072e-02   3.21865e-04  -8.14269e-03\n",
        "   3  Sigma        1.86855e+00   1.81794e-02   3.62057e-05   2.56169e-01\n",
        " FCN=1651.44 FROM MIGRAD    STATUS=CONVERGED      90 CALLS          91 TOTAL\n",
        "                     EDM=2.78326e-09    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     5.87197e+01   9.27825e-01   1.19079e-02  -8.82179e-05\n",
        "   2  Mean         9.01877e-04   1.33314e-02   2.64047e-04   2.18805e-03\n",
        "   3  Sigma        1.30172e+00   1.56898e-02   3.97537e-05  -3.36498e-02\n",
        " FCN=6143.06 FROM MIGRAD    STATUS=CONVERGED      75 CALLS          76 TOTAL\n",
        "                     EDM=3.31362e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     3.70039e+02   1.96280e+00   4.99135e-02   4.10710e-06\n",
        "   2  Mean         8.77574e-02   6.12634e-03   2.34171e-04  -2.37809e-02\n",
        "   3  Sigma        1.73614e+00   6.88032e-03   2.94564e-05  -6.79465e-01\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# deltaD0_0_3.Fit(\"gaus\")\n",
      "cd0 = ROOT.TCanvas(\"c\", \"c\", 800, 1200)\n",
      "cd0.Divide(2,3)\n",
      "cd0.cd(1)\n",
      "t000 = ROOT.TLatex(.58,.8,\"#scale[0.5]{Gaussian fit #sigma = 1.74#pm0.07}\")\n",
      "t000.SetNDC(True)\n",
      "setPlotStyle(deltaD0, \"#Deltad_{0} [cm]\")\n",
      "deltaD0.Draw()\n",
      "t000.Draw(\"same\")\n",
      "cd0.cd(2)\n",
      "t111 = ROOT.TLatex(.58,.8,\"#scale[0.5]{#splitline{Generated d_{0} < 10 cm}{Gaussian fit #sigma = 1.32#pm0.11}}\")\n",
      "t111.SetNDC(True)\n",
      "setPlotStyle(deltaD0_0_10, \"#Deltad_{0} [cm]\")\n",
      "deltaD0_0_10.Draw()\n",
      "t111.Draw(\"same\")\n",
      "cd0.cd(3)\n",
      "t222 = ROOT.TLatex(.58,.8,\"#scale[0.5]{#splitline{Generated d_{0} #in (10,25] cm}{Gaussian fit #sigma = 1.72#pm0.11}}\")\n",
      "t222.SetNDC(True)\n",
      "setPlotStyle(deltaD0_10_25, \"#Deltad_{0} [cm]\")\n",
      "deltaD0_10_25.Draw()\n",
      "t222.Draw(\"same\")\n",
      "cd0.cd(4)\n",
      "t333 = ROOT.TLatex(.58,.8,\"#scale[0.5]{#splitline{Generated d_{0} #in (25,55] cm}{Gaussian fit #sigma = 1.94#pm0.15}}\")\n",
      "t333.SetNDC(True)\n",
      "setPlotStyle(deltaD0_25_55, \"#Deltad_{0} [cm]\")\n",
      "deltaD0_25_55.Draw()\n",
      "t333.Draw(\"same\")\n",
      "cd0.cd(5)\n",
      "t444 = ROOT.TLatex(.58,.8,\"#scale[0.5]{#splitline{Generated d_{0} #in (55,110] cm}{Gaussian fit #sigma = 1.87#pm0.18}}\")\n",
      "t444.SetNDC(True)\n",
      "setPlotStyle(deltaD0_55_110, \"#Deltad_{0} [cm]\")\n",
      "deltaD0_55_110.Draw()\n",
      "t444.Draw(\"same\")\n",
      "cd0.cd(6)\n",
      "t555 = ROOT.TLatex(.58,.8,\"#scale[0.5]{#splitline{Generated d_{0} > 110 cm}{Gaussian fit #sigma = 1.30#pm0.16}}\")\n",
      "t555.SetNDC(True)\n",
      "setPlotStyle(deltaD0_110, \"#Deltad_{0} [cm]\")\n",
      "deltaD0_110.Draw()\n",
      "t555.Draw(\"same\")\n",
      "cd0.SaveAs(\"d0Resolution.C\")\n",
      "cd0.SaveAs(\"d0Resolution.root\")\n",
      "cd0.SaveAs(\"d0Resolution.pdf\")\n",
      "cd0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: c\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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yDWipwrUwWxVQDeXDGKZpIpGqDS1VFbn44a07vAIALk9XSxWzv6QUVOaF0yl/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\nRK5CYII=\n",
       "prompt_number": 141,
       "text": [
        "<ROOT.TCanvas object (\"c\") at 0x3a21d60>"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The resolution on the transverse impact parameter is between 1 cm and 2 cm for d0 of more than one meter."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "$p_T$ Resolution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cPtDist = ROOT.TCanvas(\"cPt\", \"cPt\", 400, 400)\n",
      "#cPtDist.Divide(2,2)\n",
      "#cPtDist.cd(1)\n",
      "setPlotStyle(genPt, \"p_{T} [GeV/c]\")\n",
      "genPt.GetYaxis().SetTitleOffset(1.5)\n",
      "#genPt.Draw()\n",
      "#cPtDist.cd(2)\n",
      "setPlotStyle(recoPt, \"p_{T} [GeV/c]\")\n",
      "#recoPt.Draw()\n",
      "#cPtDist.cd(3)\n",
      "recoPt.Draw(\"E\")\n",
      "genPt.Draw(\"sameE\")\n",
      "genPt.SetLineColor(2)\n",
      "genPt.SetMarkerStyle(22)\n",
      "genPt.SetMarkerSize(0.7)\n",
      "genPt.SetMarkerColor(2)\n",
      "genPt.SetLineColor(2)\n",
      "recoPt.SetMarkerStyle(24)\n",
      "recoPt.SetMarkerSize(0.7)\n",
      "legPt = ROOT.TLegend(0.4,0.6,0.8,0.8)\n",
      "legPt.SetBorderSize(0)\n",
      "legPt.SetFillColor(0)\n",
      "legPt.AddEntry(genD0,\"Generated p_{T}\",\"pl\")\n",
      "legPt.AddEntry(recoD0,\"Reconstructed p_{T}\",\"pl\")\n",
      "legPt.Draw(\"same\");\n",
      "cPtDist.SaveAs(\"PtDistributions.C\")\n",
      "cPtDist.SaveAs(\"PtDistributions.root\")\n",
      "cPtDist.SaveAs(\"PtDistributions.pdf\")\n",
      "cPtDist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: cPt\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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AcAWPZtmsknbbynjn0c6gleGeRyKRX/7yly+88MI3v/nN7du3b9q0qRlp8Bg/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\ngg==\n",
       "prompt_number": 176,
       "text": [
        "<ROOT.TCanvas object (\"cPt\") at 0x43562d0>"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%rootprint\n",
      "deltaPtOverPt_0_10.Fit(\"gaus\")\n",
      "deltaPtOverPt_10_25.Fit(\"gaus\")\n",
      "deltaPtOverPt_25_55.Fit(\"gaus\")\n",
      "deltaPtOverPt_55_110.Fit(\"gaus\")\n",
      "deltaPtOverPt_110.Fit(\"gaus\")\n",
      "deltaPtOverPt.Fit(\"gaus\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " FCN=3763.86 FROM MIGRAD    STATUS=CONVERGED     128 CALLS         129 TOTAL\n",
        "                     EDM=4.95659e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.10429e+02   1.83923e+00   3.29130e-02  -2.15901e-04\n",
        "   2  Mean         2.53013e-02   7.15758e-03   1.39828e-04  -1.51748e-01\n",
        "   3  Sigma        4.45857e-01   6.29723e-03   3.59713e-05   4.89435e-01\n",
        " FCN=6101.44 FROM MIGRAD    STATUS=CONVERGED     142 CALLS         143 TOTAL\n",
        "                     EDM=2.64479e-08    STRATEGY= 1  ERROR MATRIX UNCERTAINTY   2.5 per cent\n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     2.51838e+02   2.67155e+00   8.11292e-03   4.58099e-05\n",
        "   2  Mean        -1.14011e-01   3.79735e-03  -1.36295e-05  -8.98262e-02\n",
        "   3  Sigma        3.55729e-01   2.82717e-03  -3.38449e-06   1.40419e-01\n",
        " FCN=4669.47 FROM MIGRAD    STATUS=CONVERGED     136 CALLS         137 TOTAL\n",
        "                     EDM=3.34814e-08    STRATEGY= 1  ERROR MATRIX UNCERTAINTY   2.4 per cent\n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     2.11838e+02   2.56502e+00   1.85619e-02   2.10926e-04\n",
        "   2  Mean        -1.61912e-01   3.88304e-03   4.75318e-06   2.27454e-02\n",
        "   3  Sigma        3.86967e-01   3.90231e-03  -1.77773e-05   2.29942e-01\n",
        " FCN=2785.12 FROM MIGRAD    STATUS=CONVERGED     146 CALLS         147 TOTAL\n",
        "                     EDM=2.5613e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.24585e+02   1.95773e+00   3.07028e-02   4.77927e-05\n",
        "   2  Mean        -1.77054e-01   4.94860e-03   1.19133e-04   4.81062e-02\n",
        "   3  Sigma        4.40343e-01   6.38134e-03   4.06573e-05  -1.32458e-02\n",
        " FCN=2632.23 FROM MIGRAD    STATUS=CONVERGED     121 CALLS         122 TOTAL\n",
        "                     EDM=5.7239e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.05850e+02   1.84863e+00   2.89990e-02   3.85865e-04\n",
        "   2  Mean        -1.81707e-01   5.71034e-03   1.13853e-04   1.67925e-01\n",
        "   3  Sigma        3.89685e-01   5.79383e-03   3.61146e-05  -2.64249e-01\n",
        " FCN=23464.7 FROM MIGRAD    STATUS=CONVERGED     145 CALLS         146 TOTAL\n",
        "                     EDM=1.59185e-10    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     8.75810e+02   5.05293e+00   2.64895e-01  -2.53554e-06\n",
        "   2  Mean        -1.21175e-01   2.19430e-03   1.04473e-04   8.18800e-03\n",
        "   3  Sigma        3.36738e-01   1.41984e-03   2.60973e-05  -5.10938e-02\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cPt = ROOT.TCanvas(\"cPt\", \"cPt\", 800, 1200)\n",
      "cPt.Divide(2,3)\n",
      "cPt.cd(1)\n",
      "t00 = ROOT.TLatex(.32,.7,\"#scale[0.8]{Gaussian fit #sigma = 0.337#pm0.001}\")\n",
      "t00.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt.Draw()\n",
      "t00.Draw(\"same\")\n",
      "cPt.cd(2)\n",
      "t11 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated d_{0} < 10 cm}{Gaussian fit #sigma = 0.446#pm0.006}}\")\n",
      "t11.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_0_10, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_0_10.Draw()\n",
      "t11.Draw(\"same\")\n",
      "cPt.cd(3)\n",
      "t22 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated d_{0} #in (10,25] cm}{Gaussian fit #sigma = 0.356#pm0.003}}\")\n",
      "t22.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_10_25, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_10_25.Draw()\n",
      "t22.Draw(\"same\")\n",
      "cPt.cd(4)\n",
      "t33 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated d_{0} #in (25,55] cm}{Gaussian fit #sigma = 0.387#pm0.004}}\")\n",
      "t33.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_25_55, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_25_55.Draw()\n",
      "t33.Draw(\"same\")\n",
      "cPt.cd(5)\n",
      "t44 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated d_{0} #in (55,110] cm}{Gaussian fit #sigma = 0.440#pm0.006}}\")\n",
      "t44.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_55_110, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_55_110.Draw()\n",
      "t44.Draw(\"same\")\n",
      "cPt.cd(6)\n",
      "t55 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated d_{0} > 110 cm}{Gaussian fit #sigma = 0.390#pm0.006}}\")\n",
      "t55.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_110, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_110.Draw()\n",
      "t55.Draw(\"same\")\n",
      "cPt.SaveAs(\"PtResolutionVsD0.C\")\n",
      "cPt.SaveAs(\"PtResolutionVsD0.root\")\n",
      "cPt.SaveAs(\"PtResolutionVsD0.pdf\")\n",
      "cPt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: cPt\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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APAV1X8ChbNVUnVOpDgAAkJ2tpIpECgAiAiJwLdz++wotSQEcjqtN4CJs1VQB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\nTkBSBQAAcAKSKgAAgBP8fxcKcQCER988AAAAAElFTkSuQmCC\n",
       "prompt_number": 173,
       "text": [
        "<ROOT.TCanvas object (\"cPt\") at 0x4309ba0>"
       ]
      }
     ],
     "prompt_number": 173
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cc = ROOT.TCanvas()\n",
      "deltaPtOverPt_110.DrawNormalized()\n",
      "deltaPtOverPt_110.SetLineColor(1)\n",
      "deltaPtOverPt_55_110.DrawNormalized(\"same\")\n",
      "deltaPtOverPt_55_110.SetLineColor(2)\n",
      "deltaPtOverPt_25_55.DrawNormalized(\"same\")\n",
      "deltaPtOverPt_25_55.SetLineColor(3)\n",
      "deltaPtOverPt_10_25.DrawNormalized(\"same\")\n",
      "deltaPtOverPt_10_25.SetLineColor(4)\n",
      "deltaPtOverPt_0_10.DrawNormalized(\"same\")\n",
      "deltaPtOverPt_0_10.SetLineColor(5)\n",
      "cc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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jS0//G8NEcSFpmlZcBIduM12h3wv61Tr0vQYAABSM7zbTs9lMWjaEDOGsv5/+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\nZc5VFQAAAABJRU5ErkJggg==\n",
       "prompt_number": 22,
       "text": [
        "<ROOT.TCanvas object (\"c1_n7\") at 0x3a22800>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For larger d0 value the peak is shifting towards negative values. The selection cuts mitigate the effect of the d0-dependent bias. In particular note that there is a pt cut on the reconstructed muon which prevents the reconstructed pt from being too low."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%rootprint\n",
      "deltaPtOverPt_pt_0_80.Fit(\"gaus\")\n",
      "deltaPtOverPt_pt_80_130.Fit(\"gaus\")\n",
      "deltaPtOverPt_pt_130_180.Fit(\"gaus\")\n",
      "deltaPtOverPt_pt_180_230.Fit(\"gaus\")\n",
      "deltaPtOverPt_pt_230_300.Fit(\"gaus\")\n",
      "deltaPtOverPt_pt_300.Fit(\"gaus\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " FCN=1801.65 FROM MIGRAD    STATUS=CONVERGED     156 CALLS         157 TOTAL\n",
        "                     EDM=4.70219e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     7.73886e+01   1.75154e+00   2.15980e-02  -8.13888e-04\n",
        "   2  Mean        -1.42753e-04   6.14069e-03   1.05731e-04   7.01808e-02\n",
        "   3  Sigma        3.51977e-01   7.09971e-03   3.66023e-05  -5.07144e-01\n",
        " FCN=3315 FROM MIGRAD    STATUS=CONVERGED     143 CALLS         144 TOTAL\n",
        "                     EDM=1.25209e-09    STRATEGY= 1  ERROR MATRIX UNCERTAINTY   2.4 per cent\n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.62629e+02   2.27714e+00   5.59521e-03  -1.40578e-05\n",
        "   2  Mean        -6.56978e-02   4.38807e-03  -1.83263e-05  -5.17962e-03\n",
        "   3  Sigma        3.42806e-01   3.78103e-03   9.02170e-07   2.81314e-02\n",
        " FCN=3054.17 FROM MIGRAD    STATUS=CONVERGED     148 CALLS         149 TOTAL\n",
        "                     EDM=8.05732e-11    STRATEGY= 1  ERROR MATRIX UNCERTAINTY   1.3 per cent\n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.35755e+02   2.16815e+00   1.36801e-03   1.01542e-05\n",
        "   2  Mean        -1.17764e-01   4.85686e-03   1.06153e-06  -1.16441e-03\n",
        "   3  Sigma        4.43271e-01   6.81385e-03  -4.00871e-07   1.07110e-02\n",
        " FCN=3121.47 FROM MIGRAD    STATUS=CONVERGED     159 CALLS         160 TOTAL\n",
        "                     EDM=1.13329e-10    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.18576e+02   2.10132e+00   3.07848e-02  -9.46010e-06\n",
        "   2  Mean        -1.55693e-01   5.40014e-03   1.38299e-04  -2.25710e-03\n",
        "   3  Sigma        4.75852e-01   8.58733e-03   4.89038e-05  -2.78383e-03\n",
        " FCN=4630.75 FROM MIGRAD    STATUS=CONVERGED     154 CALLS         155 TOTAL\n",
        "                     EDM=7.59366e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.54566e+02   2.23414e+00   4.69176e-02   2.02815e-05\n",
        "   2  Mean        -1.67283e-01   5.14179e-03   1.22646e-04  -3.56896e-02\n",
        "   3  Sigma        3.80299e-01   4.56559e-03   3.52536e-05   2.74085e-01\n",
        " FCN=5275.21 FROM MIGRAD    STATUS=CONVERGED     158 CALLS         159 TOTAL\n",
        "                     EDM=2.65329e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  Constant     1.45039e+02   2.47768e+00   4.80161e-02   3.06912e-05\n",
        "   2  Mean        -2.08440e-01   6.38539e-03   1.38701e-04   5.85732e-02\n",
        "   3  Sigma        3.90482e-01   6.01262e-03   4.04425e-05  -9.35733e-02\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccc = ROOT.TCanvas(\"ccc\", \"ccc\", 800, 1200)\n",
      "ccc.Divide(2,3)\n",
      "ccc.cd(1)\n",
      "t1 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated p_{T} < 80 GeV/c}{Gaussian fit #sigma = 0.352#pm0.007}}\")\n",
      "t1.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_pt_0_80, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_pt_0_80.Draw()\n",
      "t1.Draw(\"same\")\n",
      "ccc.cd(2)\n",
      "t2 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated p_{T} #in (80,130] GeV/c}{Gaussian fit #sigma = 0.343#pm0.004}}\")\n",
      "t2.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_pt_80_130, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_pt_80_130.Draw()\n",
      "t2.Draw(\"same\")\n",
      "ccc.cd(3)\n",
      "t3 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated p_{T} #in (130,180] GeV/c}{Gaussian fit #sigma = 0.443#pm0.006}}\")\n",
      "t3.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_pt_130_180, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_pt_130_180.Draw()\n",
      "t3.Draw(\"same\")\n",
      "ccc.cd(4)\n",
      "t4 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated p_{T} #in (180,230] GeV/c}{Gaussian fit #sigma = 0.476#pm0.009}}\")\n",
      "t4.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_pt_180_230, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_pt_180_230.Draw()\n",
      "t4.Draw(\"same\")\n",
      "ccc.cd(5)\n",
      "t5 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated p_{T} #in (230,300] GeV/c}{Gaussian fit #sigma = 0.380#pm0.005}}\")\n",
      "t5.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_pt_230_300, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_pt_230_300.Draw()\n",
      "t5.Draw(\"same\")\n",
      "ccc.cd(6)\n",
      "t6 = ROOT.TLatex(.32,.7,\"#scale[0.8]{#splitline{Generated p_{T} > 300 GeV/c}{Gaussian fit #sigma = 0.390#pm0.006}}\")\n",
      "t6.SetNDC(True)\n",
      "setPlotStyle(deltaPtOverPt_pt_300, \"#Deltap_{T}/p_{T}^{gen}\")\n",
      "deltaPtOverPt_pt_300.Draw()\n",
      "t6.Draw(\"same\")\n",
      "ccc.SaveAs(\"PtResolutionVsPt.C\")\n",
      "ccc.SaveAs(\"PtResolutionVsPt.root\")\n",
      "ccc.SaveAs(\"PtResolutionVsPt.pdf\")\n",
      "ccc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: ccc\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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AXqKlCsATyZj44Z9VVck/w3Lvvbxo1ratzaQqmJ2z8vADeDZaqgA8Szp0ft/3Mhy2/FNa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       "prompt_number": 174,
       "text": [
        "<ROOT.TCanvas object (\"ccc\") at 0x4357160>"
       ]
      }
     ],
     "prompt_number": 174
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    }
   ],
   "metadata": {}
  }
 ]
}