Rivet analyses

title: CMS_2021_I1963239

Measurement of inclusive and Mueller-Navelet dijet cross sections and their ratios at 2.76 TeV

Experiment: CMS (LHC)

Inspire ID: 1963239

Status: VALIDATED

Authors: - cms-pag-conveners-smp@cern.ch - Anatolii Egorov - Victor Kim - Victor Murzin - Vadim Oreshkin - Vladimir Gavrilov - Grigory Pivovarov - Grigory Safronov

References: - JHEP 03 (2022) 189 - Expt page: CMS-FSQ-13-004 - arXiv: 2111.04605

Beams: p+ p+

Beam energies: (1380.0, 1380.0)GeV

Run details: - pp QCD interactions at $\sqrt{s} = 2.76$ TeV. Data collected by CMS during the year 2013.

This is a measurement of the differential cross sections of inclusive and Mueller-Navelet dijet production as a function of the absolute distance in rapidity, $\Delta y$, between jets. The ratios of inclusive to exclusive dijet production, the Mueller-Navelet to exclusive dijet production, as well as the ratios of inclusive to exclusive with veto and Mueller-Navelet to exclusive with veto is also measured. These measurements were performed with the CMS detector in proton-proton collisions at $\sqrt{s} = 2.76$ TeV for jets with $p_T > 35$ GeV and $|y| < 4.7$, with integrated luminosity of 5.4pb^-1. The measured observables are corrected for detector effects.

Source code:CMS_2021_I1963239.cc

```c++ // -- C++ --

include "Rivet/Analysis.hh"

include "Rivet/Projections/FastJets.hh"

include "Rivet/Projections/FinalState.hh"

namespace Rivet {

/// @brief Measurement of inclusive and Mueller-Navelet dijet cross sections and their ratios at 2.76 TeV class CMS_2021_I1963239 : public Analysis { public:

/// Constructor
RIVET_DEFAULT_ANALYSIS_CTOR(CMS_2021_I1963239);


/// @name Analysis methods
///@{

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

  // Initialise and register projections

  // The basic final-state projection:
  // all final-state particles within
  // the given eta acceptance
  const FinalState fs(Cuts::abseta < 5.2);

  // The final-state particles declared above are clustered using FastJet with
  // the anti-kT algorithm and a jet-radius parameter 0.5
  FastJets jetfs(fs, JetAlg::ANTIKT, 0.5);
  declare(jetfs, "jets");

  // Book histograms
  // specify custom binning
  // take binning from reference data using HEPData ID (digits in "d01-x01-y01" etc.)

  book(_h["inclusive"], 7, 1, 1);
  book(_h["MN"], 8, 1, 1);
  book(_s["R_incl"], 9, 1, 1);
  book(_s["R_incl_veto"], 10, 1, 1);
  book(_s["R_MN"], 11, 1, 1);
  book(_s["R_MN_veto"], 12, 1, 1);

  // Temporary histograms (directly instantiated)
  book(_h["exclusive"], "_exclusive", refData(7, 1, 1));
  book(_h["exclusive_veto"], "_exclusive_veto", refData(7, 1, 1));
}


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

  // Retrieve clustered jets, sorted by pT, with a minimum pT cut
  Jets jets20 = apply<FastJets>(event, "jets").jetsByPt(Cuts::pT > 20 * GeV && Cuts::absrap < 4.7);
  Jets jets35 = apply<FastJets>(event, "jets").jetsByPt(Cuts::pT > 35 * GeV && Cuts::absrap < 4.7);

  if (jets35.size() < 2) return;

  // Loop over jet pairs
  double deltaY_MN = 0.0;
  for (size_t ij1 = 0; ij1 < jets35.size(); ++ij1) {
    for (size_t ij2 = ij1 + 1; ij2 < jets35.size(); ++ij2) {
      const double deltaY = fabs(jets35[ij1].rapidity() - jets35[ij2].rapidity());
      // Exclusive dijet case:
      if (jets35.size() == 2) {
        _h["exclusive"]->fill(deltaY);
        //Exclusive with veto 20 GeV dijet case:
        if (jets20.size() == 2) {
          _h["exclusive_veto"]->fill(deltaY);
        }
      }
      // Inclusive jets case:
      _h["inclusive"]->fill(deltaY);
      // Mueller-Navelet:
      if (deltaY > deltaY_MN) deltaY_MN = deltaY;
    }
  }
  // Fill histogram with MN dijets Delta y
  _h["MN"]->fill(deltaY_MN);
}


/// Normalise histograms etc., after the run
void finalize() {

  // Calculate ratios
  efficiency(_h["exclusive"], _h["inclusive"], _s["R_incl"]);
  efficiency(_h["exclusive"], _h["MN"], _s["R_MN"]);
  efficiency(_h["exclusive_veto"], _h["inclusive"], _s["R_incl_veto"]);
  efficiency(_h["exclusive_veto"], _h["MN"], _s["R_MN_veto"]);

  transform(*_s["R_incl"], _invert);
  transform(*_s["R_MN"], _invert);
  transform(*_s["R_incl_veto"], _invert);
  transform(*_s["R_MN_veto"], _invert);


  scale(_h["inclusive"],
        crossSection() / picobarn / sumOfWeights());           // norm to generated cross-section in pb
  scale(_h["MN"], crossSection() / picobarn / sumOfWeights()); // norm to generated cross-section in pb
}

///@}


/// @name Histograms
///@{
map<string, Histo1DPtr> _h;
map<string, Estimate1DPtr> _s;
///@}

private:

/// Reciprocal function with div-by-zero protection, for inverting the efficiency measure
static double _invert(double x) {
  return (x > 0) ? 1 / x : 0;
}

};

RIVET_DECLARE_PLUGIN(CMS_2021_I1963239);

} ```