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ATLAS_2014_I1307243

Measurements of jet vetoes and azimuthal decorrelations in dijet events produced in pp collisions at $\sqrt{s}$ = 7 TeV using the ATLAS detector
Experiment: ATLAS (LHC)
Inspire ID: 1307243
Status: VALIDATED
Authors:
  • James Robinson
References: Beams: p+ p+
Beam energies: (3500.0, 3500.0) GeV
Run details:
  • pp -> jet jet x. sqrt(s) = 7000 GeV. Jets with pT > 60, 50 GeV

Additional jet activity in dijet events is measured using $pp$ collisions at ATLAS at a centre-of-mass energy of 7 TeV, for jets reconstructed using the anti-kt algorithm with radius parameter R=0.6. This is done using variables such as the fraction of dijet events without an additional jet in the rapidity interval bounded by the dijet subsystem and correlations between the azimuthal angles of the dijets. They are presented, both with and without a veto on additional jet activity in the rapidity interval, as a function of the mean transverse momentum of the dijets and of the rapidity interval size. The double differential dijet cross section is also measured as a function of the interval size and the azimuthal angle between the dijets. These variables probe differences in the approach to resummation of large logarithms when performing QCD calculations. The data are compared to POWHEG, interfaced to the PYTHIA 8 and HERWIG parton shower generators, as well as to HEJ with and without interfacing it to the ARIADNE parton shower generator. None of the theoretical predictions agree with the data across the full phase-space considered; however, POWHEG+PYTHIA 8 and HEJ+ARIADNE are found to provide the best agreement with the data. These measurements use the full data sample collected with the ATLAS detector in 7 TeV $pp$ collisions at the LHC and correspond to integrated luminosities of 36.1 pb$^-1$ and 4.5 fb$^-1$ for data collected during 2010 and 2011 respectively.

Source code: ATLAS_2014_I1307243.cc
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// -*- C++ -*-
#include "Rivet/Analysis.hh"
#include "Rivet/Tools/Logging.hh"
#include "Rivet/Projections/FastJets.hh"
#include "Rivet/Tools/BinnedHistogram.hh"

namespace Rivet {


  /// @brief ATLAS azimuthal decorrelation with jet veto analysis
  /// @author James Robinson <james.robinson@cern.ch>
  class ATLAS_2014_I1307243 : public Analysis {
  public:

    /// Constructor
    DEFAULT_RIVET_ANALYSIS_CTOR(ATLAS_2014_I1307243);


    /// Book histograms and initialise projections before the run
    void init() {

      _dy_max = 8; _years = { 2010, 2011 };
      _Qnoughts = { 20., 30., 40., 50., 60., 70., 80., 90., 100. };
      /// Initialise and register projections here
      FastJets fastJets(FinalState(), FastJets::ANTIKT, 0.6, JetAlg::Muons::ALL, JetAlg::Invisibles::ALL);
      declare(fastJets, "AntiKt6JetsWithInvisibles");


      /// Book histograms
      for (const string& cat : { "inclusive", "gap" }) {
        const size_t offset = (cat == "gap") ? 1 : 0;

        // Temporary inclusive and gap histograms
        book(_aux_dy[cat], "_" + cat + "_dy", refData(1, 1, 1));
        book(_aux_pTbar[cat], "_" + cat + "_pTbar", refData(2, 1, 1));

        book(_h_C2C1_dy[cat],    7 + 4 * offset, 1, 1, false);
        book(_h_C2C1_pTbar[cat], 8 + 4 * offset, 1, 1, false);

        // Azimuthal moment histograms
        book(_p_cosDeltaPhi_dy[cat],        5 + 4 * offset, 1, 1);
        book(_p_cosDeltaPhi_pTbar[cat],     6 + 4 * offset, 1, 1);
        book(_p_cosTwoDeltaPhi_dy[cat],    37 + 2 * offset, 1, 1);
        book(_p_cosTwoDeltaPhi_pTbar[cat], 38 + 2 * offset, 1, 1);

        // Gap fraction vs. Q0 and cross-section in dy slices
        _s_gapFrac_Q0.resize(_dy_max);
        for (size_t dyLow = 0; dyLow < _dy_max; ++dyLow ) {
          const string hname("_" + cat + "_dySlice_" + toString(dyLow) + "_" + toString(dyLow+1) + "_Q0");
          { Histo1DPtr tmp; _aux_Q0_dySlices[cat].add(dyLow, dyLow+1, book(tmp, hname, refData(29+dyLow, 1, 1))); }
          { Histo1DPtr tmp; _h_dphi_dySlices[cat].add(dyLow, dyLow+1, book(tmp, 13+(_dy_max*offset)+dyLow, 1, 1)); }
          if (!offset)  book(_s_gapFrac_Q0[dyLow], 29 + dyLow, 1, 1); // only book once
        }

      }

      // Number of jets in rapidity interval
      book(_s_gapFrac_dy,     1, 1, 1);
      book(_s_gapFrac_pTbar,  2, 1, 1);
      book(_p_nGapJets_dy,    3, 1, 1);
      book(_p_nGapJets_pTbar, 4, 1, 1);
    }


    /// Perform the per-event analysis
    void analyze(const Event& event) {

      for (size_t reg = 0; reg < 2; ++reg) {

        // Retrieve all anti-kt R=0.6 jets 
        const double maxRap = reg? 2.4 : 4.4;
        const Jets& akt6Jets = apply<JetAlg>(event, "AntiKt6JetsWithInvisibles").jetsByPt(Cuts::absrap < maxRap);
        // If there are fewer than 2 jets then bail
        if ( akt6Jets.size() < 2 ) { vetoEvent; }

        // Require jets to be above {60, 50} GeV
        if ( akt6Jets[0].pT() < 60*GeV || akt6Jets[1].pT() < 50*GeV ) { vetoEvent; }

        // Identify gap boundaries
        const double yMin = std::min(akt6Jets[0].rap(), akt6Jets[1].rap());
        const double yMax = std::max(akt6Jets[0].rap(), akt6Jets[1].rap());

        // Determine azimuthal decorrelation quantities
        const double dy = yMax - yMin;
        const double dphi = mapAngle0ToPi(deltaPhi(akt6Jets[0], akt6Jets[1]));
        const double pTbar = 0.5*(akt6Jets[0].pT() + akt6Jets[1].pT())/GeV;

        // Impose minimum dy for the 2011 phase space
        if ( _years[reg] == 2011 && dy < 1.0 ) { vetoEvent; }

        // Determine gap quantities
        size_t nGapJets = 0;
        double maxGapQ0 = 0.;
        const double vetoScale = reg? 30*GeV : 20*GeV;
        for (const Jet& jet : akt6Jets) {
          if (!inRange(jet.rap(), yMin, yMax, OPEN, OPEN))  continue;
          const double pT = jet.pT()/GeV;
          if (pT > vetoScale) { ++nGapJets; }
          if (pT > maxGapQ0) { maxGapQ0 = pT; }
        }

        // Fill histograms
        fillHists(_years[reg], nGapJets, { dy, pTbar, dphi, maxGapQ0 });
      }
      return;
    }

    void fillHists(const size_t region, const size_t nGapJets, const vector<double>& vars) {
      assert(vars.size() == 4);
      const double dy = vars[0];
      const double pTbar = vars[1];
      const double dphi = vars[2];
      const double maxGapQ0 = vars[3];
      // Determine gap category
      vector<string> categories = {"inclusive"};
      if (!nGapJets) { categories += string("gap"); }

      // Fill histograms relevant for comparison with 2010 data
      if (region == _years[0]) {
        // Fill inclusive and gap histograms
        for (const string& cat : categories) {
          _aux_dy[cat]->fill(dy);
          _h_dphi_dySlices[cat].fill(dy, dphi/M_PI);
          _p_cosDeltaPhi_dy[cat]->fill(dy, cos(M_PI - dphi));
          _p_cosTwoDeltaPhi_dy[cat]->fill(dy, cos(2*dphi));
        }
        // Fill profiled nGapJets
        _p_nGapJets_dy->fill(dy, nGapJets);
        // Fill Q0 histograms - can fill multiple points per event
        for (const double Q0 :  _Qnoughts) {
          _aux_Q0_dySlices["inclusive"].fill(dy, Q0);
          if (maxGapQ0 <= Q0) { _aux_Q0_dySlices["gap"].fill(dy, Q0); }
        }

      // Fill histograms relevant for comparison with 2011 data
      } 
      else if (region == _years[1]) {
        // Fill inclusive and gap histograms
        for (const string& cat : categories) {
          _aux_pTbar[cat]->fill(pTbar);
          _p_cosDeltaPhi_pTbar[cat]->fill(pTbar, cos(M_PI - dphi));
          _p_cosTwoDeltaPhi_pTbar[cat]->fill(pTbar, cos(2*dphi));
        }
        // Fill profiled nGapJets
        _p_nGapJets_pTbar->fill(pTbar, nGapJets);
      }
      return;
    }

    /// Normalise histograms etc., after the run
    void finalize() {
      // Normalise cross-section plots to correct cross-section
      const double ySpan = 1.0; // all dy spans are 1
      const double sf = crossSection() / picobarn / sumOfWeights();
      for (const string& cat : { "inclusive", "gap" }) {
        _h_dphi_dySlices[cat].scale(sf/ySpan/M_PI, this);
        // Create C2/C1 scatter from profiles
        divide(_p_cosTwoDeltaPhi_dy[cat],    _p_cosDeltaPhi_dy[cat],    _h_C2C1_dy[cat]);
        divide(_p_cosTwoDeltaPhi_pTbar[cat], _p_cosDeltaPhi_pTbar[cat], _h_C2C1_pTbar[cat]);
      }

      // Fill simple gap fractions
      efficiency(_aux_dy["gap"], _aux_dy["inclusive"], _s_gapFrac_dy);
      efficiency(_aux_pTbar["gap"], _aux_pTbar["inclusive"], _s_gapFrac_pTbar);

      // Register and fill Q0 gap fractions
      for (size_t dyLow = 0; dyLow < _dy_max; ++dyLow) {
        efficiency(_aux_Q0_dySlices["gap"].histos()[dyLow], _aux_Q0_dySlices["inclusive"].histos()[dyLow], _s_gapFrac_Q0[dyLow]);
      }
    }


  private:

    /// Member variables
    vector<size_t> _years;
    vector<double> _Qnoughts;

    size_t _dy_max;

    /// auxiliary histograms for gap fractions
    map<string, Histo1DPtr> _aux_dy;
    map<string, Histo1DPtr> _aux_pTbar;
    map<string, BinnedHistogram> _aux_Q0_dySlices;

    // Gap fractions
    Scatter2DPtr _s_gapFrac_dy, _s_gapFrac_pTbar;
    vector<Scatter2DPtr> _s_gapFrac_Q0;

    // Number of jets in rapidity interval
    Profile1DPtr _p_nGapJets_dy;
    Profile1DPtr _p_nGapJets_pTbar;

    // Azimuthal moment histograms
    map<string, Profile1DPtr> _p_cosDeltaPhi_dy;
    map<string, Profile1DPtr> _p_cosDeltaPhi_pTbar;
    map<string, Profile1DPtr> _p_cosTwoDeltaPhi_dy;
    map<string, Profile1DPtr> _p_cosTwoDeltaPhi_pTbar;
    map<string, Scatter2DPtr> _h_C2C1_dy;
    map<string, Scatter2DPtr> _h_C2C1_pTbar;

    // Cross-section vs. deltaPhi in deltaY slices
    map<string, BinnedHistogram> _h_dphi_dySlices;
  };

  // The hook for the plugin system
  DECLARE_RIVET_PLUGIN(ATLAS_2014_I1307243);

}