gistfile1.txt

{
 "metadata": {
  "name": "MasterClassD0"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "TESHEP practical work : Measuring the D0 lifetime at the LHC"
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Introduction"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "The Large Hadron Collider (LHC) is not only a tool with which to search for exotic new particles, but also a factory of particles whose existence is in no doubt but whose precise properties are not yet known well enough. One example are charmed hadrons, i.e. hadrons which contain a c quark, which were first discovered more than 30 years ago. Approximately one in every ten LHC interactions produces a charmed hadron, and the LHCb experiment at the LHC has already recorded over one billion signal events in various decay channels. In order to extract information from such large signal samples, it is necessary to achieve excellent control over backgrounds. This set of exercises is designed to teach you how to..."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "The data sample used for this exercise consists of candidate $D^{0}$ mesons found in a sample of randomly collected LHC interactions during\n2010 datataking. The mesons have been preselected using loose criteria so that you begin the exercise with a visible signal. A $D^{0}$ meson consists\nof a charm anti-quark and an up quark. The mesons are fully reconstructed decaying in the mode $D^{0}\\to K^+ \\pi^-$, where the final state particles\nare a kaon ($K^+$) consisting of a strange anti-quark and an up quark, and a pion ($\\pi^-$) which consists of a down quark and an up anti-quark."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "![lhcb_pic](files/LHCb_image_from_VELO_poster_withaxes.png \"LHCb picture\")\n\nFigure 1. The LHCb detector. The $\\textbf{Z}$ axis is the direction of the LHC beamline.\n\nBefore discussing the data further, it is worth taking the time to familiarize yourselves with the LHCb detector, shown in Fig.1.\nIt is a single-arm forward spectrometer covering the angular range between $0.7^\\circ$ and $15^\\circ$ relative to the beamline.\nIn what follows ``transverse'' means transverse to the LHC beamline. The detector includes a high-precision tracking system consisting of a\nsilicon-strip vertex detector surrounding the $pp$ interaction region, a large-area silicon-strip detector located upstream of a dipole\nmagnet with a bending power of about $4{\\rm\\,Tm}$, and three stations of silicon-strip detectors and straw drift tubes placed\ndownstream. The combined tracking system has a momentum resolution $\\Delta p/p$ that varies from 0.4% at 5$~$GeV$/c$ to 0.6% at 100$~$GeV$/c$,\nan impact parameter resolution of 20 $\\mu$m for tracks with high transverse momentum, and a decay time resolution\nof 50$~$fs. Charged hadrons are identified using two ring-imaging Cherenkov detectors.\n..."
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Exercise 1 : fitting the mass distribution"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "The object of this exercise is to fit the distribution of the $\\textbf{D0_MM}$ variable and extract the signal yield and purity. If you plot this\nvariable within the range (1816-1914)$~$MeV, you will see a peak (signal) on top of a flat distribution (background). The peak should be described\nby a Gaussian function, whose mean corresponds to the mass of the $D^0$ and whose width is determined by the experimental resolution of the LHCb\ndetector. You should use the RooFit fitting framework for the following exercise."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Let's plot mass distribution of signal and background"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import ROOT",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "inputfile = ROOT.TFile(\"TESHEP_Pedagogical_Data.root\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "inputchain = inputfile.Get(\"DecayTree\")\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nameoffitvar = \"D0_MM\"\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rangeoffitvarlow = 1816.\nrangeoffitvarhigh = 1914.\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "backgroundrangelow = 1890.\nbackgroundrangehigh = 1914.\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nsigmasigbox = 2.\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "massvar = ROOT.RooRealVar(\"D0_MM\",\"D0_MM\",rangeoffitvarlow,rangeoffitvarhigh)\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "mydataset = ROOT.RooDataSet(\"DecayTreeDataSet\",\"DecayTreeDataSet\",ROOT.RooArgSet(massvar))\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "for entry in range(0,inputchain.GetEntries()) :\n  inputchain.GetEntry(entry)\n  if inputchain.__getattr__(nameoffitvar) > rangeoffitvarhigh or \\\n     inputchain.__getattr__(nameoffitvar) < rangeoffitvarlow :\n    continue\n  massvar.setVal(inputchain.__getattr__(nameoffitvar))\n  mydataset.add(ROOT.RooArgSet(massvar))\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "gausMean_B \t= ROOT.RooRealVar(\"gausMean_B\",\"gausMean_B\",(rangeoffitvarlow+rangeoffitvarhigh)/2.,rangeoffitvarlow,rangeoffitvarhigh)\ngausWidth_B \t= ROOT.RooRealVar(\"gausWidth_B\",\"gausWidth_B\",15.,1.,50.)\nchebOne_B \t= ROOT.RooRealVar(\"chebOne_B\",\"chebOne_B\",0.1,-5.,5.)\ngaussian_B \t= ROOT.RooGaussian(\"gaussian_B\",\"gaussian_B\",massvar,gausMean_B,gausWidth_B)\ncheb_B     \t= ROOT.RooChebychev(\"cheb_B\",\"cheb_B\",massvar,ROOT.RooArgList(chebOne_B))",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "N_Sig_B = ROOT.RooRealVar(\"N_Sig_B\",\"N_Sig_B\",10000,0,250000)\nN_Bkg_B = ROOT.RooRealVar(\"N_Bkg_B\",\"N_Bkg_B\",10000,0,500000)\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "totalpdf_B = ROOT.RooAddPdf(\"totalpdf_B\",\"totalpdf_B\",ROOT.RooArgList(gaussian_B,cheb_B),ROOT.RooArgList(N_Sig_B,N_Bkg_B))\ntotalpdf_B.fitTo(mydataset,ROOT.RooFit.Extended())\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": "<ROOT.RooFitResult object at 0x(nil)>"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import rootnotes\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "canv_Dmass = rootnotes.default_canvas()\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "frame_Dmass = massvar.frame()\nmydataset.plotOn(frame_Dmass,ROOT.RooFit.Binning(100))\ntotalpdf_B.plotOn(frame_Dmass)\nframe_Dmass.Draw()\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "canv_Dmass",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
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hgj8AAFCE+6ouQNmsCpkBAEBpCpn7T/frSZNVaMIZfRtgvi8NAABQofw7O0NJOCVVujpK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\nQRUAAEAO/j8BQqfs15FHugAAAABJRU5ErkJggg==\n",
       "prompt_number": 23,
       "text": "<ROOT.TCanvas object (\"icanvas\") at 0x63cee10>"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "massvar.setRange(\"integralRangeAll\", rangeoffitvarlow, rangeoffitvarhigh)\nmassvar.setRange(\"integralRangeSignal\", gausMean_B.getVal()-nsigmasigbox*gausWidth_B.getVal(), gausMean_B.getVal()+nsigmasigbox*gausWidth_B.getVal())\nmassvar.setRange(\"integralRangeBackground\", backgroundrangelow, backgroundrangehigh)\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "integralall    = cheb_B.createIntegral(ROOT.RooArgSet(massvar),ROOT.RooFit.NormSet(ROOT.RooArgSet(massvar)),ROOT.RooFit.Range(\"integralRangeAll\"))\nintegralsignal = cheb_B.createIntegral(ROOT.RooArgSet(massvar),ROOT.RooFit.NormSet(ROOT.RooArgSet(massvar)),ROOT.RooFit.Range(\"integralRangeSignal\"))\nintegralback   = cheb_B.createIntegral(ROOT.RooArgSet(massvar),ROOT.RooFit.NormSet(ROOT.RooArgSet(massvar)),ROOT.RooFit.Range(\"integralRangeBackground\"))",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "normalizedIntegralValue_S = integralsignal.getVal()/integralall.getVal()\nnormalizedIntegralValue_B = integralback.getVal()/integralall.getVal()\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "canv_Dmass",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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vjY6wMAwfPHiQnYBgIPTWOAAop7mgSuYs2Hjrn2qO0sk2pzAMZSAVBIHrusvl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\nAABABQiqAAAAKkBQVV5+HtFeoApdQBW6gCp0AVX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       "prompt_number": 28,
       "text": "<ROOT.TCanvas object (\"icanvas\") at 0x63cee10>"
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print \"Signal range =\",gausMean_B.getVal()-nsigmasigbox*gausWidth_B.getVal(),\"-\",gausMean_B.getVal()+nsigmasigbox*gausWidth_B.getVal()\nprint \"Background in signal range =\",normalizedIntegralValue_S*N_Bkg_B.getVal()\nprint \"Background in background range =\",normalizedIntegralValue_B*N_Bkg_B.getVal()\n\nprint \"Background scale factor =\", normalizedIntegralValue_S/normalizedIntegralValue_B",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Signal range = 1849.15867581 - 1879.81505391\nBackground in signal range = 21579.3283653\nBackground in background range = 16197.0885418\nBackground scale factor = 1.33229674639\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "normalizedIntegralValue_S/normalizedIntegralValue_B",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": "1.3322967463941657"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}