BackgroundSystematic-NewMethod-Marco-Copy0.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from math import sqrt, exp\n",
      "import ROOT\n",
      "from ROOT import TFile, TDirectory, TH1F, TCanvas, TLegend, TLine, TText, TPad, TF1, TGraph, TGraphErrors\n",
      "import rootnotes\n",
      "import rootprint\n",
      "ROOT.gStyle.SetErrorX(0) #To remove horizontal bars...\n",
      "#gStyle.SetTitleYSize(0.02)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Background"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have 2D d0Sig vs. LxySig plots for both control region of data. We sliced LxySig axis into the bins each of which has a width of 0.25. Note that at least for this purpose, we cut on d0Sig, absolute value of which is to be less than several numbers. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def LxySig_distribution(x, fit=True, input_alpha=0):\n",
      "    # rootprint\n",
      "    final_region_dist = ROOT.TH1F(\"final_region\"+str(x), \"\", 160, 0, 40)\n",
      "    control_region_dist = ROOT.TH1F(\"control_region\"+str(x), \"\", 160, 0, 40)\n",
      "\n",
      "    # Some Histogram Settings\n",
      "    control_region_dist.GetXaxis().SetTitle(\"L_{xy}/#sigma_{L_{xy}}\")\n",
      "    control_region_dist.GetYaxis().SetTitle(\"Number of events\")\n",
      "    control_region_dist.SetTitle(\"#scale[0.8]{CMS Preliminary #sqrt{s} = 8 TeV, L = 20.5 fb^{-1}}\")\n",
      "\n",
      "    control_region_dist.SetStats(0)\n",
      "    control_region_dist.SetMarkerStyle(21)\n",
      "    control_region_dist.SetMarkerColor(1)\n",
      "    control_region_dist.SetLineColor(1)\n",
      "    control_region_dist.Sumw2\n",
      "    \n",
      "    final_region_dist.GetXaxis().SetTitle(\"L_{xy}/#sigma_{L_{xy}}\")\n",
      "    final_region_dist.GetYaxis().SetTitle(\"Number of events\")\n",
      "    final_region_dist.SetTitle(\"#scale[0.8]{CMS Preliminary #sqrt{s} = 8 TeV, L = 20.5 fb^{-1}}\")\n",
      "\n",
      "    final_region_dist.SetStats(0)\n",
      "    final_region_dist.SetMarkerStyle(24)\n",
      "    final_region_dist.SetMarkerColor(1)\n",
      "    final_region_dist.SetLineColor(1)\n",
      "    final_region_dist.Sumw2\n",
      "    \n",
      "\n",
      "    # Loop to write down new histos\n",
      "    for i in range(1, 160):\n",
      "        # The bin width is 0.25, to start at 4 we start from bin 16 (+1 since it counts from 1 not 0).\n",
      "        # This gives the Lxy/sigma distribution for d0/sigma > 4.\n",
      "        # print i,nMinus1_looseCollCuts.Integral(i, i, 4*x+1, 160), nMinus1_looseCollCuts.Integral(i, i, 4*x+1, 160)\n",
      "        control_region_dist.SetBinContent(i, nMinus1_looseControlCollCuts.Integral(i, i, 4*x+1, 160))\n",
      "        final_region_dist.SetBinContent(i, nMinus1_looseCollCuts.Integral(i, i, 4*x+1, 160))\n",
      "    control_region_dist.Rebin(2)\n",
      "    final_region_dist.Rebin(2)\n",
      "\n",
      "    # Line\n",
      "    line = TLine(6, 0, 6, 100)\n",
      "    line.SetLineColor(4)\n",
      "    line.SetLineStyle(2)\n",
      "\n",
      "    # Text\n",
      "    text1 = TText(7, 1, \"Excluded Region\")\n",
      "    text2 = TText(9, 100, \"Data\")\n",
      "\n",
      "    # Add fit function\n",
      "    # Actual one\n",
      "    f1 = TF1(\"f1\",\"[0]*exp([1]*x)\", 3, 9)\n",
      "    f1.SetLineColor(2)\n",
      "    f1.SetParameter(1, -0.3376)\n",
      "    \n",
      "    # +20%\n",
      "    f2 = TF1(\"f2\",\"[0]*exp([1]*x)\", 3, 20)\n",
      "\n",
      "    # Fit histogram\n",
      "    if fit:\n",
      "        control_region_dist.Fit(\"f1\", \"R\")\n",
      "        alpha = f1.GetParameter(1)\n",
      "        norm = f1.GetParameter(0)/(-alpha*exp(-7*alpha))\n",
      "    else:\n",
      "        alpha = input_alpha\n",
      "        norm = control_region_dist.Integral(control_region_dist.FindBin(7), control_region_dist.FindBin(20))\n",
      "    \n",
      "    print \"norm control region =\", norm\n",
      "    print \"alpha control region =\", alpha\n",
      "    print \"Chi2/ndf control region =\", str(f1.GetChisquare())+\"/\"+str(f1.GetNDF()), \"=\", f1.GetChisquare()/f1.GetNDF()\n",
      "    \n",
      "    #f1.SetParameter(0, -alpha*exp(-7*alpha)*norm)\n",
      "    #f1.SetParameter(1, alpha)\n",
      "    \n",
      "    canvas=TCanvas(\"\", \"\", 800, 400)\n",
      "    canvas.Divide(2,1)\n",
      "    canvas.cd(1)\n",
      "    control_region_dist.Draw(\"e\")\n",
      "    if not fit:\n",
      "        control_region_dist.GetYaxis().SetNdivisions(3)\n",
      "\n",
      "    #alpha_high = alpha*1.2\n",
      "    #f2.SetParameter(0, -alpha_high*exp(-7*alpha_high)*norm)\n",
      "    #f2.SetParameter(1, alpha_high)\n",
      "\n",
      "    \n",
      "    #alpha_low = alpha*0.8\n",
      "    f1.Draw(\"fcsame\")\n",
      "    #f3 = TF1(\"f3\",\"[0]*exp([1]*x)\", 7, 20)\n",
      "    #f3.SetLineColor(4)\n",
      "    #f3.SetParameter(0, -2*alpha_low*exp(-7*alpha_low)*norm)\n",
      "    #f3.SetParameter(1, alpha_low)\n",
      "    # f4.SetParameter(0, alpha*exp(7*alpha))\n",
      "    # f4.SetParameter(1, -alpha)\n",
      "    #f3.Draw(\"fcsame\")\n",
      "\n",
      "    # Legend\n",
      "    leg = TLegend(0.45,0.7,0.85,0.85)\n",
      "    leg.SetFillColor(0)\n",
      "    leg.SetLineColor(0)\n",
      "    # leg.SetTextAlign(12)\n",
      "    leg.AddEntry(\"f1\", \"Background fit control region\", \"l\")\n",
      "    #leg.AddEntry(\"f3\", \"#splitline{Background fit shape}{variation and normalization x 2}\", \"l\")\n",
      "    leg.Draw(\"same\")\n",
      "    # line.Draw()\n",
      "    # text1.Draw()\n",
      "    # text2.Draw()\n",
      "    \n",
      "    canvas.cd(2)\n",
      "\n",
      "    # Add fit function\n",
      "    # Actual one\n",
      "    f4 = TF1(\"f4\",\"[0]*exp([1]*x)\", 3, 9)\n",
      "    f4.SetLineColor(2)\n",
      "    f4.SetParameter(1, -0.3376)\n",
      "    final_region_dist.Fit(\"f4\", \"R\")\n",
      "    final_region_dist.Draw(\"e\")\n",
      "\n",
      "    if fit:\n",
      "        f4.Draw(\"fcsame\")\n",
      "        alpha_s = f4.GetParameter(1)\n",
      "        norm_s = f4.GetParameter(0)/(-alpha*exp(-7*alpha))\n",
      "    \n",
      "    # Legend\n",
      "    leg4 = TLegend(0.45,0.7,0.85,0.85)\n",
      "    leg4.SetFillColor(0)\n",
      "    leg4.SetLineColor(0)\n",
      "    # leg.SetTextAlign(12)\n",
      "    leg4.AddEntry(\"f4\", \"Background fit signal region\", \"l\")\n",
      "    #leg.AddEntry(\"f3\", \"#splitline{Background fit shape}{variation and normalization x 2}\", \"l\")\n",
      "    leg4.Draw(\"same\")\n",
      "    # line.Draw()\n",
      "    # text1.Draw()\n",
      "    # text2.Draw()\n",
      "\n",
      "    print \"norm signal region =\", norm_s\n",
      "    print \"alpha signal region =\", alpha_s\n",
      "    print \"Chi2/ndf control region =\", str(f4.GetChisquare())+\"/\"+str(f4.GetNDF()), \"=\", f4.GetChisquare()/f4.GetNDF()\n",
      "\n",
      "    canvas.Draw()\n",
      "    # canvas.SetLogy(True)\n",
      "\n",
      "    # canvas\n",
      "    return canvas, control_region_dist, f1, f2, f4, alpha, leg, final_region_dist, leg4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%rootprint\n",
      "inputFileData = TFile.Open(\"Data22Jan_combined_2saMu_final_FORBACKGROUNDSYSTEMATIC.root\")\n",
      "\n",
      "#nMinus1_looseCollCuts = inputFileData.Get(\"nMinus1_leptonD0Sig_vs_LxySig_looseCollCuts\")\n",
      "#nMinus1_looseControlCollCuts = inputFileData.Get(\"nMinus1_leptonD0Sig_vs_LxySig_looseControlCollCuts\")\n",
      "nMinus1_looseCollCuts = inputFileData.Get(\"MinLeptonD0Sig_vs_MinLxySig_finalCollCuts\")\n",
      "nMinus1_looseControlCollCuts = inputFileData.Get(\"MinLeptonD0Sig_vs_MinLxySig_controlCollCuts\")\n",
      "\n",
      "resultnotkrej4 = LxySig_distribution(4, fit=True)\n",
      "resultnotkrej4[0].SaveAs(\"No_MET_track_rejection_removed_d0_greater_4_bckg_estimation_LxySig_different_function_fit.pdf\")\n",
      "\n",
      "alpha = resultnotkrej4[5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "norm control region = 2.5755568038\n",
        "alpha control region = -0.428703451969\n",
        "Chi2/ndf control region = 3.47758550148/8 = 0.434698187685\n",
        "norm signal region = 0.948539469356\n",
        "alpha signal region = -0.267641856194\n",
        "Chi2/ndf control region = 4.11615097609/5 = 0.823230195219\n",
        "Info in <TCanvas::Print>: pdf file No_MET_track_rejection_removed_d0_greater_4_bckg_estimation_LxySig_different_function_fit.pdf has been created\n",
        " ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  p0           2.21980e+01   1.61044e+01   4.72520e-03  -8.70361e-06\n",
        "   2  p1          -4.28703e-01   1.47328e-01   4.32504e-05  -8.28798e-04\n",
        " FCN=4.11615 FROM MIGRAD    STATUS=CONVERGED     108 CALLS         109 TOTAL\n",
        "                     EDM=7.60617e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
        "  EXT PARAMETER                                   STEP         FIRST   \n",
        "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
        "   1  p0           8.17518e+00   9.07305e+00   2.33420e-03  -3.83305e-04\n",
        "   2  p1          -2.67642e-01   2.38656e-01   5.98635e-05  -1.02133e-02\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: \n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "canvas = TCanvas(\"\", \"\", 800, 400)\n",
      "canvas.Divide(2,1)\n",
      "canvas.cd(1)\n",
      "nMinus1_looseControlCollCuts.Draw(\"COLZ\")\n",
      "canvas.cd(2)\n",
      "nMinus1_looseCollCuts.Draw(\"COLZ\")\n",
      "canvas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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MzABzqsa5/Wf+BNCq63q73e5lVjcH1uv1YWgMQFIBZxmhqDp3/sTDPXajQxjAabe6vnf+\nG/883jaourtIPaF0NzAilstlNzeGIalgci6aVKcY7em/0+dPNA8a5GDh2l30Mqyq6ugbptIq3XF7\n6gk84dgkFUzF6JfhOLf/0hCwe4bL5bLtq+uoA8nwDarDvUsq4EQjFFXZzp8AaEkq4FzjFFUxyPyJ\nF0VExCfNQ1sAjspzpheQs7weA24fVG63POVBZUUVDOPtg8p/e/5/+fOsIuhE/SYVMIwBkir3IBBV\nkL9rK6oO9ZVU3YGfQSD0a4Ck8tl/AAA9GO2z/063t+zEPMa1MGk+oeVQL0nV7UvpUcETDZ9UEyiq\nVFGQm72rUo0VkgryM3xSuf0HANADRRUAQA8UVQAAPVBUAQD04KqLqhfFm5VgAACe6KqLKgCAvkxg\nSYXLrVNlGRh4nHueTP7g/Hf6+VMPJRtW1IPcDJ9UEyiqZBPkxjpVhyQV5MY6VQzEfDIA6NcEOlVc\nglufANAvnSoAgB4oqgAAeqCoAgDowQTmVHlQGXLjcb9DkgpyM3xSTaCokk2QG0sqHJJUkBtLKvTJ\nqgHAdEkwmJw5F1UAAIOZwO2/R7MUEzBdEgwmR6cKAKAHc+5Unag7a8HQEAB4HEUVAMBJqqqKiLIs\ny7JsN97e3kbEy5cvJ1BUnbj6S2o4PaLVpDsF57rnyeTvnf9OHz31ULLR4zpVj04zoKv3pCqKYrlc\nlmW5Wq2Wy2Vd12njzc3NbrcrimICRZXVXyA3F1r9pa7r7uAvjQiTvXFhhiQV5KbfpEolVPp3VVXp\n3W5vbxeLxcuXLyPi2bNn85mo/kljYAfTtlqtUmAl6/V6vGPp3ynrTqXvkWaQp81ms7dlt9stFov0\nerFYTKBTBcxeWZbb7ba7pa7r5XLZbVYBXErxrfd+S7dZXpblcrmMiN1ud3NzkzaOXFRNutUP9Kid\nmrC35eic0IH1lVSnNJ80qGAcza8f+mqn5Kqqar1e393dHR3yjXn7b96tfiAiovjWQ/88qO1d7WXF\nwCQVkJRlWdd10zRtRbVYLHa7XXq92+3G6VRp9cO1OHn8t6eqqm4gVFU1fF0lqYBWXdfb7XZv8nu3\nqPr444/HKapybvUDuSnLcpT+kKQCWoeB0DTNy5cvi6J49uxZRCwWi4ye/ruv1V88aO9NfK47XMJZ\nl2EvyrJsG0JVVaU5oTnoJakOyS54uosmVVVVzbvS9qZpbm5ubm5uXr9+ncvTfw+0+q3+AqN7+DK8\nRF1VVVWqWlIRk0kOSCrI2fBJlTx//jy9yKWo6npKq3+AZ2csdgwX0g3EsiybpkklS5532fq9KSlS\nYAZyuf2XbasfGFFu85YkFfCAXDpVebb6jzp3QJnWRwZmYEJJBQxvzKJqWq1+4DpJKuBEReYjraLI\n/QiBdJ0W8eX5/+W35nGBSyrI3wBJlcucKgCASct9dHX4AGTmB5wnTyzSr6MXZhF/OP+Nvj6PK1pS\nQYaGT6pcJqo/QDZBbvauysut/jIhkgpyM3xSTaBT9cARasBADt7OVLjqTtU8TgRmbICkyj0IRBXk\nT1E1elIZYcJ7DZBUJqoDAPQg9z7Q6OM/4L10qiQV5E+nCgBgGibw9N/edP3Th4MmGcCFeNzv0KWT\nSqDBuYZPqgkUVZrqkBtLKhySVJAbSyrsu2+mgkEb5MOcKnOqIH/mVAEATEPuoyvjP8ifTpWkgvzp\nVAEATIOiCgCgB4oqAIAeTGBJhUev/gJcyPEnk385gTy5HCvqQW6GT6oJhKAqCnJjnapDkgpyM3xS\nTaCoApglPSqYGXOqAAB6oFMFMI5eelTaXZAPnSoAgB7Mp1NluAZcIaEH+dCpAgDowQQ6VSeu/mK4\nBoOxhsIhK+pBboZPqgkUVbIJcmOdqkOSCnIzfFK5/Qfkoq7rvS1VVVVVNcKhAJxPUQXkYrVadeuq\nNKys61onDJiEMYuqIUelL4o3jwcCGSrLcq9yqqpquVxWVVXXdXox0qHpnwGnGrOoMioFkrqu92Y/\n1HVdlmV6XZblYWUzGEkFnGicomr4UeknjccDYQx/XTz0z/222223qBrgSA/l3D8DMjTO039p2NdN\nq3xGpUCf/vXB0cyDddXoJBVwllyWVNhut+2Yby+qHu6xe4wZBnDSra7f9LnH5XLZVjD51C6SCnI2\nfFLtyaWoeoAwgtE9fBleYnZRt2RZr9ebzab3XfRLUsHohk+qPbksqZBGpel1PqPSSfPAI5NWVVWa\nVlWW5XK5HGta1Z5LJJVLFWYjl07VMKPSq/rQ5Ss5TeZkb5TZNE2KhUwqqugjqQ5TyKUKs5FLUVVV\nVVEUKTrzGZUC48otCiQV8IAxi6rhR6VGhMC5+k2qwxTqpYN+VW14yFYunarEsA/In6QCjsqrqDpq\nb7p+74/YGOHBuSwmfujRSdVL+EgwODR8Uk2gqPKgMuRm76pUY4WkgvwMn1QTKKre64mtJiM8YLr0\n2iEfuaxTBQAwaXPoVBmiAdPSY3tJAEI+dKoAAE51+GkKt7e3t7e3oagCGN4nzX6HyYfVwFSsVqvD\nD1Pf7XZFUSiqAADeryzLvUcIb29vF4vFy5cvX79+vVgsJjCn6kLrVL0o3hkpeoIGTnf8yeTPBz+O\nnDwxqYQP9K73pEoNqu7b7na7xWKRXk+jqLL6C+TGOlWHJBXk5oyk+uUjQ2y3293c3KTX0yiqLmRv\nXPi4YaL+FgBM3nce/B/5ySWXOVUAAI+xWCx2u116vdvtrrdT1Qs9qkfQ3oNTuFIgf92i6uOPP1ZU\nAQA8xsuXL4uiePbsWVz5nKqjDA0H4McLXffFjisF8rQ3/71pms8++ywinj9/rqgCAHi858+fpxcT\nKKqeuPrLWc2npwwNdbm4HtZQOPTopDorNOQMnG74pJpAUWX1F8iNdaoOSSrIzfBJNYGi6okGG9IZ\nOwI9OtqUkjOQM+tUAQD0YP6dqu4HvxvkAVMhr2BydKoAAHow/06V0R6Qgyc+uOe5P8ifThUAQA+K\nzB8DPnwA8vvRhOEajOrwwmyapvjz89/oq9wj6ERHfyCjHAnQGj6pJnD7r/dsOtpF11qH01mn6pAq\nCnJjnSrgGlVV9cAfASYhl977fZFaFLkcIXCfdJ0+paleFMVyuSzLMm3OtqiSVDBdT0+q9+8ikyC4\nL1JFFeSvl6JqEle6pILpuq6i6uiR9BtV3YlTL4oeZlCZiQXRU1F1d3dX13VZltm2qWKopAIuYYCi\nKqMlFaqqyjxPgbN9VTz0T0RE1HWdXpRluV6v2z5QniQVcJ8sRld1Xa9Wq7u7u4hYr9fL5bIN2ffO\n1c/h+GH2TrkS+xr/Zdv16TGpNLnhEoZMquMHkGF4dSM123gFWv021ady1T8lqRRVMLzruv13ohfF\nO5+RPOOdwpVIN9TS67b3M2+fNGdUVAPnj7iDR8uiqLrCSAVaVVVtt9uiKMqyXK1Wm81m7CM6TlIB\nD8ulzZ7ugy6Xy+12u9ls2uQ6pal+biN97/v14eGJemmqpzIl81nqT0mqRODAWAa4/ZfLiupN00wi\nUoELmcS1L6mAB+TSqbrPAFNW9xavCoNIONMA47/MXSKpTowjqQUnMlEdAGAach8mHq45ccoBG7rB\nRR29ML91/iNjX2YfQSd6XFJlQmAyV8MnVS5zqh4woWyCK7F3Vb53wb1rIKkgN8Mn1QSKqkd77/Br\nsPGZgSCQLdEEfTGnCgCgB7lPaHj4mZreO0DddYQ9DwgnStfplc+pmseJwIwNkFQ6VQAAPZj2nKoL\nNZC6b6tHBQwmh9Z4DscAE6VTBQDQg9znARw+APn9aKKPCU8vCkMxeCTrVO256Ip6WkfwOMMn1QQ6\nVc27xj4cwFV5hJ8J5Gb4q3J6c6rum/B07mDuk8b4DxiT8IGZmUCnCgAgf7lPaBh49Re9K3gE61T1\nm1SCCC7BOlUAANMwvTlVF9XX0NBAE3iv+4Ki9+iQSDAMnSoAgB7kPqHhvtVfHh54db+a7p7++thz\ngqeM3rrf87jRngWxmJ+jF+aL82cqfJp9BJ3ocUn1CHpOcLrhk2oCt//aH8FhjpySL79+Wmv9cR9Z\n0z2w/ONPTB/lx/KAvXw5TK4rtJdUp//+HH6O+95Xh/ngLMM/5mf4pHL7DwCgB7n33g8fVH54LFj8\neURE8+/HvwpcQrpOr/z23wM99aT78/ltRNwzLeFcT5+iAFdigKTSqQIA6MEE5lTF2+FXd2yXpp9/\n+Y2IiE//OiLim7+MiIhvRLztV33z4B2So8O4937DJRhWwpx020WHftt5/UFEvM2xDx58z3aq02Dr\nLwCPplN1xCizbu3UTqe7U0ZxPb9gdmqnUzGBTtXej7748ybaLtTfRUTEzyLedq3iP0dExG8i3h3/\ndZdXaEeH9435Pn3y3/WJLShDTCZqroH4FHs/k29GExFffici3iTSm+1fRbztWh32qA5b5nuLvzyc\nLU/5jHmYn+GTagJF1fej6bbNvxztQIA3LKlw6HGfKQZczvBJNYGiKtrR3ncj4k0v6svv/CEi4pdf\ni4j4PxER3/zriIgvOyPCVIrdN2vhxMU/H2fvPQ0ZYfZeFG+Tqit101MudXvqERHxwVf7337Yaop7\nFq9qX9zXwRI4MDxzqgAAepDRIjEfffRRRHz44Yd/8zd/024siiK+0aReVHznbyPin+LnEfGDIv6y\nid/Ff42I+Id/joj4PCIiflnE/2rSiDD1tNJTgV9G8f1oPtl7cvCr46d/3+JYaaWKw0kPh9/ffZ+9\ndYof/uYLbZ/um1/PTmdwRk9c/aWqqogoy7Isy7PfaED3JdU3o3nTqfrXP42I/x6/j4h/LCKaX0f8\nS0TEP/zoj+/yP4r4TvPmmeWOlFTdLZ80Q/9udONusJ1O/c2vZ6czOKOLJlUunaoPP/zw1atXEfHd\n7373F7/4xdiHAwyqLMu6riNitVqlF3mSVHDN3ptUWcyp+sUvfvGrX/2qLQN/8pOfdIeA8e9/+Kf4\nekT8tyIi4n+/rT6/iPgv8fOI+N0PfxoR8cO/i4goIn74Zfzsj2tUvZnB8FV8+o34bWd+1QdfxacH\nR3JYwHZnKnx6/9SovZkNXTOe2fCIeh8O1XW93W67A8E866qHk+rLb0T8608joil+HxFF8ycREfH7\niH95M7Xqh38bEfHLf37zH/zmTTql5wFTLn0Z8dt7Vq56+CNQ7/v+++y92+HHlT79IWiYmVOSKoui\n6tWrV3/1V3+VXv/kJz/xJBFclbqul8tlel1VVbYJIKngmp2SVFkUVe9RfP0HERHxgzd/fLP5L/54\nOh91/h1RfOvNi735Cl8VaTmG7qIM98Xi3vZ20Hbf938axdGB3dHvP3Gn/W6f7ptfz04nfUZxT1P2\niiqPr4qUTm9OuPj9m+3FR39Mp87X46s3L/Zy6csouhmVgqX9Me7lzBP/+tp3+zSK3t/8cdun++bX\ns9NJn1FcOKlyL6rymUcPPMKVXMJXcpowV31dwllMVP/www/HPgRgNJk/7teSVHDNTkmqXJZUaB+A\n/Oijj169evXq1avhn69Oe7zvj5dQ1/Xe2aWdXnTXezvt7utCP+2jf5WXPtPDnTrTS+y6u6+nnGmb\nAGnuZ54T1UNSvbtTSXWJnTrTS+x6sKTKolMVET/60Y+Kovjwww9/+tOfvnr1apTnq9fr9cBRvnd2\n6Z5uXdcXnYayt9P1en25fUVEURTtX2V7zVz6TI/udMgzbS/XIc+03emlzzSpqqq7oyee6d3dXVEU\nZVkOfxmeRVKFpLrwTiVVv4ZOqiYnn3/+edM0m82mPbC7u7vlcjnM3of8abRPEGw2m7Sle6bL5fLu\n7m6AnW42m4v+eLt/lc3bn/Clz/ToTgc40/b92wMY4EwPd3rpM+3u7uh1+pQzbX8zMyep2q9Kqn53\nKql63/XASZXXRPW06MuIz1enhl5ZlpfuqKcKt3tq3V53O/wdYKdx4ZWs29/p7k4vfaZHdxqXPNPu\nibS/wJc+0/t2GpdfnXy1Wm02m9Vq1e69lzMd7A7aE0mq9FpSXWKnIan6M3xS5XL7b3TtDze19YYP\n9+122/3LHnK/6cUl7l90L5WyLNO1dOkzPbrTuPCZJqml3LaFh/k73dtpXNMxjCYAAAObSURBVP5M\ny7LcbDbdMxrrt/cKSSpJ9XSSKi53po/rfV3UXiN9lIMcZqfR6W/HUL3u7o66Lnf/4u7uLiLaRusw\nZ7q3070vXbTtnHbdDPh32t3p3sbed9p9z3aPQ55pPiSVpOp9p3tfklRP2dEoSZVjp+o6R7rL5bLb\nIB3+AMqybMcN/b5tXddN07T3KQY408Od7n219zOt67o9l8HO9OhOuy50ptvttiiKdHcmzT8d/bd3\nFJJKUvW+072vSqqn7HScpOq9TOtFDD79s7ujvfmDlxP3TP+Me4Zove+0O1NvuVz2/qM++pO89Jke\n3emlz/To78+lz/ToTi99pl1Hr9OL/vbmRlJJqn53KqkuYcikyrSoSh3CdI95sJ2mKjPtdJj/K+zt\nKO19gF+vbv+zPeVL/KjT3+NhEX/RMz2600ufaXPP78+l/04PdzrAmXb33n09wG9vbiTVMDuVVD2S\nVBc901wW/zyqPlhxboA9xqhd/VEOwE4v+v6z3OnpR3INJJWdTmunkupyO826qAJOUT7h2eC9tYYB\nLuQakirHierAWZ4yx/N65pUD47qGpFJUwQx98cUXe6+7kdR9/uUK79YBmZhfUimqYIbaNe6++OKL\nb3/72/Hu8nrt+sLdjnqKraJj8KMGrsv8kkpRBTOUPkD07u7u29/+dpo3eXd3l1LpaBe9ruv0QadN\n0yyXy/R4zqBHDFyf+SWVieoweUVx/EIuiuLHP/5x+wnt6dvSh2OkD4+r67r97K3opNh9bwjwaNeQ\nVDpVME9pgsLezNC0ynA7EHzgaZpJPGgDTN3MkkpRBfPUbaenLXd3d6vVql1tr9tdr6pqL9TaEeFU\nHroBpmhmSaWogjnoTttMEzl//OMfR0Rd13//93+fvicN6drBX/dpmrIs7+7uUrO9fR3TeYwZmITZ\nJ1V29yOByzl3CkJKrjbU0tp9h/++xKECV2u6SaVTBVchxcrhx42dpaqqNL7s6aAA3jH1pFJUwVVI\nUfX0SZ3L5bL95Kxe3hCgNfWkcvsPuFdVVev1Os0YPWykZ/g8M3CF8kkqmQg80lQ+4hS4ZkMm1deG\n2Q0wM6aoA/kbOKl0qgAAemCiOgBADxRVAAA9UFQBAPRAUQUA0ANFFQBADxRVAAA9UFQBAPRAUQUA\n0ANFFQBADxRVAAA9UFQBAPRAUQUA0ANFFQBADxRVAAA9UFQBAPRAUQUA0ANFFQBADxRVAAA9UFQB\nAPTg/wOlwEGC42vpWQAAAABJRU5ErkJggg==\n",
       "prompt_number": 19,
       "text": [
        "<ROOT.TCanvas object at 0x7f94e759fc30>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print resultnotkrej4[2].Integral(12, 10000)\n",
      "print resultnotkrej4[4].Integral(12, 10000)\n",
      "print resultnotkrej4[2].Integral(7, 10000)\n",
      "print resultnotkrej4[4].Integral(7, 10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.301892035299\n",
        "1.2306047408\n",
        "2.57555666188\n",
        "4.69132467241\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result4 = LxySig_distribution(4, input_alpha=alpha)\n",
      "result4[0].SaveAs(\"No_MET_track_rejection_removed_d0_greater_4_bckg_estimation_LxySig_different_function.pdf\")\n",
      "result4[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "norm control region = 2.5755568038\n",
        "alpha control region = -0.428703451969\n",
        "Chi2/ndf control region = 3.47758550148/8 = 0.434698187685\n",
        "norm signal region = 0.948539469356\n",
        "alpha signal region = -0.267641856194\n",
        "Chi2/ndf control region = 4.11615097609/5 = 0.823230195219\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: final_region4 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: control_region4 (Potential memory leak).\n",
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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AlAjos+6TKq11WZZ2fDqLKQCAUlwBBPpHxOy/OI7N8lRGdTIgAABALwi6AG86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\nAABJRU5ErkJggg==\n",
       "prompt_number": 21,
       "text": [
        "<ROOT.TCanvas object at 0x7f94e7b280f0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "d0Sig > 4"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "exp(-alpha*x) where alpha = 0.3376+-0.0424"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result4[2].Integral(12, 10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "0.30189203529891295"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result4[4].Integral(12, 10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "1.2306047408005005"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result4[3].Integral(12, 10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print resultnotkrej4[1].GetNbinsX()\n",
      "print resultnotkrej4[1].GetXaxis().GetXmax()\n",
      "print resultnotkrej4[1].FindBin(7)\n",
      "print resultnotkrej4[1].Integral(resultnotkrej4[1].FindBin(7), resultnotkrej4[1].FindBin(20))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "80\n",
        "40.0\n",
        "15\n",
        "8.0\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We normalize the shape to the number of events observed in the same range of the fit from the Lxy/sigma distribution after the full selection in the signal and constrol regions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lxy_final_signal = inputFileData.Get(\"nMinus1_DileptonAbsLxySig_finalCollCuts\")\n",
      "lxy_final_control = inputFileData.Get(\"nMinus1_DileptonAbsLxySig_controlCollCuts\")\n",
      "c_final = TCanvas(\"c_final\", \"c_final\", 800, 400)\n",
      "c_final.Divide(2,1)\n",
      "c_final.cd(1)\n",
      "lxy_final_control.Draw()\n",
      "c_final.cd(2)\n",
      "lxy_final_signal.Draw()\n",
      "c_final"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 26,
       "text": [
        "<ROOT.TCanvas object (\"c_final\") at 0x7f94e75b8f00>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "signal_integral = lxy_final_signal.Integral(3, 41)\n",
      "control_integral = lxy_final_control.Integral(3, 41)\n",
      "print \"signal integral =\", signal_integral\n",
      "print \"control integral =\", control_integral"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "signal integral = 50.0\n",
        "control integral = 210.0\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_s = result4[4]\n",
      "f_c = result4[2]\n",
      "alpha_s = f_s.GetParameter(1)\n",
      "alpha_c = f_c.GetParameter(1)\n",
      "print \"alpha_s =\", alpha_s\n",
      "print \"alpha_c =\", alpha_c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "alpha_s = -0.267641856194\n",
        "alpha_c = -0.428703451969\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "signal_norm = -signal_integral*alpha_s/(exp(alpha_s*3) - exp(alpha_s*10))\n",
      "control_norm = -control_integral*alpha_c/(exp(alpha_c*3) - exp(alpha_c*10))\n",
      "print signal_norm\n",
      "print control_norm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "35.289659702\n",
        "342.834840173\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "signal_background = -signal_norm*exp(alpha_s*12)/alpha_s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "control_background = -control_norm*exp(alpha_c*12)/alpha_c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"background in signal region =\", signal_background\n",
      "print \"background in control region =\", control_background\n",
      "print \"systematic =\", abs(1-control_background/signal_background)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "background in signal region = 5.31213204684\n",
        "background in control region = 4.66364459914\n",
        "systematic = 0.122076680696\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Conclusions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When fitting the background in the control region and using the range 3-9 (same as the signal) we have:\n",
      "- background in signal region = 0.0563967215108\n",
      "- background in control region = 0.0234939315973\n",
      "- systematic = 0.583416713455\n",
      "\n",
      "In the control region we can also perform a fit of the full range 3-20. If we do so we obtain:\n",
      "- background in signal region = 0.0563967215108\n",
      "- background in control region = 0.0281358031678\n",
      "- systematic = 0.501109241564\n",
      "\n",
      "We take the largest of the two (58%) as systematic uncertainty.\n",
      "\n",
      "Note that this is an uncertainty on a small background expectation. The expectations as estimated above are < 0.1 event for both the signal and the control region. Even assuming a 100% uncertainty would still lead to a small expected background."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}