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CMS_2018_I1682495

Jet mass in dijet events in $pp$ collisions at 13 TeV
Experiment: CMS (LHC)
Inspire ID: 1682495
Status: VALIDATED
Authors:
  • cms-pag-conveners-smp@cern.ch
  • Salvatore Rappoccio
References:
  • JHEP 11 (2018) 113
  • DOI 10.1007/JHEP11(2018)113
  • hep-ex arXiv:1807.05974
  • inspirehep 1682495
  • http://cms-results.web.cern.ch/cms-results/public-results/publications/SMP-16-010/index.html
Beams: p+ p+
Beam energies: (6500.0, 6500.0) GeV
Run details:
  • $pp$ QCD interactions at $\sqrt{s} = 13$ TeV. Data collected by CMS during the year 2015.

Measurements of the differential jet cross section are presented as a function of jet mass in dijet events, in bins of jet transverse momentum, with and without a jet grooming algorithm. The data have been recorded by the CMS Collaboration in proton-proton collisions at the LHC at a center-of-mass energy of 13\text{TeV} and correspond to an integrated luminosity of 2.3\fbinv. The absolute cross sections show slightly different jet transverse momentum spectra in data and Monte Carlo event generators for the settings used. Removing this transverse momentum dependence, the normalized cross section for ungroomed jets is consistent with the prediction from Monte Carlo event generators for masses below 30\% of the transverse momentum. The normalized cross section for groomed jets is measured with higher precision than the ungroomed cross section. Semi-analytical calculations of the jet mass beyond leading logarithmic accuracy are compared to data, as well as predictions at leading order and next-to-leading order, which include parton showering and hadronization. Overall, in the normalized cross section, the theoretical predictions agree with the measured cross sections within the uncertainties for masses from 10 to 30\% of the jet transverse momentum.

Source code: CMS_2018_I1682495.cc
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// -*- C++ -*-
#include "Rivet/Analysis.hh"
#include "Rivet/Projections/FinalState.hh"
#include "Rivet/Projections/VetoedFinalState.hh"
#include "Rivet/Projections/FastJets.hh"
#include "Rivet/Tools/fjcontrib/SoftDrop.hh"

namespace Rivet {



  // Soft-drop and ungroomed jet mass measurement
  class CMS_2018_I1682495 : public Analysis {
  public:

    /// @name Constructors etc.
    //@{

    /// Constructor
    CMS_2018_I1682495()
      : Analysis("CMS_2018_I1682495"),
        _softdrop(fjcontrib::SoftDrop(0, 0.1, 0.8) ) // parameters are beta, zcut, R0
    {    }

    //@}


    /// @name Analysis methods
    //@{

    /// Book histograms and initialise projections before the run
    void init() {
      // define a projection that keeps all the particles up to |eta|=5
      const FinalState fs(Cuts::abseta < 5.);

      // use FastJet, anti-kt(R=0.8) to do the clustering
      addProjection(FastJets(fs, FastJets::ANTIKT, 0.8), "JetsAK8");

      // Histograms
      for (size_t i = 0; i < N_PT_BINS_dj; ++i ) {
        _h_ungroomedJetMass_dj[i][0] = bookHisto1D(i+1+0*N_PT_BINS_dj, 1, 1); // Ungroomed mass, absolute
        _h_sdJetMass_dj[i][0]        = bookHisto1D(i+1+1*N_PT_BINS_dj, 1, 1); // Groomed mass, absolute
        _h_ungroomedJetMass_dj[i][1] = bookHisto1D(i+1+2*N_PT_BINS_dj, 1, 1); // Ungroomed mass, normalized
        _h_sdJetMass_dj[i][1]        = bookHisto1D(i+1+3*N_PT_BINS_dj, 1, 1); // Groomed mass, normalized
      }
    }


    // Find the pT histogram bin index for value pt (in GeV), to hack a 2D histogram equivalent
    /// @todo Use a YODA axis/finder alg when available
    size_t findPtBin(double ptJ) {      
      for (size_t ibin = 0; ibin < N_PT_BINS_dj; ++ibin) {
        if (inRange(ptJ, ptBins_dj[ibin], ptBins_dj[ibin+1])) return ibin;
      }
      return N_PT_BINS_dj;
    }


    /// Perform the per-event analysis
    void analyze(const Event& event) {
      const double weight = event.weight();

      // Look at events with >= 2 jets
      auto jetsAK8 = applyProjection<FastJets>(event, "JetsAK8").jetsByPt(Cuts::pT > 200*GeV and Cuts::abseta < 2.4);
      if (jetsAK8.size() < 2) vetoEvent;

      // Get the leading two jets
      const fastjet::PseudoJet& j0 = jetsAK8[0].pseudojet();
      const fastjet::PseudoJet& j1 = jetsAK8[1].pseudojet();

      // Calculate delta phi and the pt asymmetry
      double deltaPhi = Rivet::deltaPhi( j0.phi(), j1.phi() );
      double ptasym = (j0.pt() - j1.pt()) / (j0.pt() + j1.pt());
      if (deltaPhi < 2.0 ) vetoEvent;
      if (ptasym > 0.3) vetoEvent;

      // Find the appropriate pT bins and fill the histogram
      const size_t njetBin0 = findPtBin(j0.pt()/GeV);
      const size_t njetBin1 = findPtBin(j1.pt()/GeV);
      if (njetBin0 < N_PT_BINS_dj && njetBin1 < N_PT_BINS_dj) {
        for ( size_t jbin = 0; jbin < N_CATEGORIES; jbin++ ){
          _h_ungroomedJetMass_dj[njetBin0][jbin]->fill(j0.m()/GeV, weight);
          _h_ungroomedJetMass_dj[njetBin1][jbin]->fill(j1.m()/GeV, weight);
        }
      }

      // Now run the substructure algs...
      fastjet::PseudoJet sd0 = _softdrop(j0);
      fastjet::PseudoJet sd1 = _softdrop(j1);
      // ... and repeat
      if (njetBin0 < N_PT_BINS_dj && njetBin1 < N_PT_BINS_dj) {
        for ( size_t jbin = 0; jbin < N_CATEGORIES; jbin++ ){
          _h_sdJetMass_dj[njetBin0][jbin]->fill(sd0.m()/GeV, weight);
          _h_sdJetMass_dj[njetBin1][jbin]->fill(sd1.m()/GeV, weight);
        }
      }
    }


    /// Normalise histograms etc., after the run
    void finalize() {
      // Normalize the normalized cross section histograms to unity,
      for (size_t i = 0; i < N_PT_BINS_dj; ++i) {
        normalize(_h_ungroomedJetMass_dj[i][1]);
        normalize(_h_sdJetMass_dj[i][1]);
      }
      // Normalize the absolute cross section histograms to xs * lumi.
      for (size_t i = 0; i < N_PT_BINS_dj; ++i) {
        scale(_h_ungroomedJetMass_dj[i][0],   crossSection()/picobarn / sumOfWeights() / (ptBins_dj[i+1]-ptBins_dj[i]) );
        scale(_h_sdJetMass_dj[i][0],          crossSection()/picobarn / sumOfWeights() / (ptBins_dj[i+1]-ptBins_dj[i]) );
      }
    }
    //@}


  private:

    /// @name FastJet grooming tools (configured in constructor init list)
    //@{
    const fjcontrib::SoftDrop _softdrop;
    //@}


    /// @name Histograms
    //@{
    enum { PT_200_260_dj=0,
           PT_260_350_dj,
           PT_350_460_dj,
           PT_460_550_dj,
           PT_550_650_dj,
           PT_650_760_dj,
           PT_760_900_dj,
           PT_900_1000_dj,
           PT_1000_1100_dj,
           PT_1100_1200_dj,
           PT_1200_1300_dj,
           PT_1300_Inf_dj,
           N_PT_BINS_dj };
    static const int N_CATEGORIES=2;
    const double ptBins_dj[N_PT_BINS_dj+1]= { 200., 260., 350., 460., 550., 650., 760., 900., 1000., 1100., 1200., 1300., 13000.};

    Histo1DPtr _h_ungroomedJet0pt, _h_ungroomedJet1pt;
    Histo1DPtr _h_sdJet0pt, _h_sdJet1pt;
    // Here, store both the absolute (index 0) and normalized (index 1) cross sections.
    Histo1DPtr _h_ungroomedJetMass_dj[N_PT_BINS_dj][2];
    Histo1DPtr _h_sdJetMass_dj[N_PT_BINS_dj][2];
    //@}


  };


  // The hook for the plugin system
  DECLARE_RIVET_PLUGIN(CMS_2018_I1682495);


}