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ALICE_2022_I1868463

$\Sigma_c^{0,+,++}$ and $\Lambda_c$ from $\Sigma_c^{0,+,++}$ analysis
Experiment: ALICE (LHC)
Inspire ID: 1868463
Status: VALIDATED
Authors:
  • Marco Giacalone
References:
  • Phys.Rev.Lett. 128 (2022) 012001, 2022
  • DOI:10.1103/PhysRevLett.128.012001
  • arXiv: 2106.08278
Beams: p+ p+
Beam energies: (6500.0, 6500.0) GeV
Run details:
  • Minimum bias events

The $p_\mathrm{T}$-differential production cross sections of prompt ${D^{0}}$, ${\Lambda_{c}^{+}}$, and ${\Sigma_{c}^{0,++}}(2455)$ charmed hadrons are measured at midrapidity ($|y|<0.5$) in pp collisions at $\sqrt{s}=13$ TeV. This is the first measurement of ${\Sigma_{c}^{0,++}}$ production in hadronic collisions. Assuming the same production yield for the three $\Sigma_{c}^{0,+,++}$ isospin states, the baryon-to-meson cross section ratios $\Sigma_{c}^{0,+,++}/D^{0}$ and $\Lambda_{c}^{+}/D^{0}$ are calculated in the transverse momentum ($p_{T}$) intervals $2<p_{T}<12$ GeV/$c$ and $1<p_{T}<24$ GeV/$c$. Values significantly larger than in $e^{+}e^{-}$ collisions are observed, indicating for the first time that baryon enhancement in hadronic collisions also extends to the $\mathrm{\Sigma_{c}}$. The feed-down contribution to $\Lambda_{c}^{+}$ production from $\Sigma_{c}^{0,+,++}$ is also reported and is found to be larger than in $e^{+}e^{-}$ collisions. The data are compared with predictions from event generators and other phenomenological models, providing a sensitive test of the different charm-hadronisation mechanisms implemented in the models.

Source code: ALICE_2022_I1868463.cc
  1// -*- C++ -*-
  2#include "Rivet/Analysis.hh"
  3#include "Rivet/Projections/UnstableParticles.hh"
  4
  5
  6namespace Rivet {
  7
  8
  9  /// @brief Measurement of prompt charm hadrons production in proton-proton Collisions at 13 TeV
 10  class ALICE_2022_I1868463 : public Analysis {
 11  public:
 12
 13    /// Constructor
 14    RIVET_DEFAULT_ANALYSIS_CTOR(ALICE_2022_I1868463);
 15
 16
 17    /// @name Analysis methods
 18    ///@{
 19
 20    /// Book histograms and initialise projections before the run
 21    void init() {
 22
 23      // Initialise and register projections
 24
 25      // The basic final-state projection:
 26      // all final-state particles within
 27      // the given eta acceptance
 28      const UnstableParticles up(Cuts::absrap < 0.5);
 29      declare(up, "up");
 30
 31      book(_h_D0,1,1,1);
 32      book(_h_Lc,2,1,1);
 33      book(_h_Sc,3,1,1);
 34      book(_h_LcfromSc,4,1,1);
 35      book(_h_LcD0,5,1,1);
 36      book(_h_ScD0,6,1,1);
 37      book(_h_LcfromScLc,7,1,1);
 38      book(_h_D04Sc,"TMP/D04Sc",refData(3,1,1));
 39      book(_h_Lc4Ratio, "TMP/Lc4Ratio", refData(4,1,1));
 40
 41    }
 42
 43
 44    /// Perform the per-event analysis
 45    void analyze(const Event& event) {
 46
 47      const UnstableParticles& up = apply<UnstableParticles>(event, "up");
 48
 49      for (const Particle& p : up.particles()) {
 50        if(p.fromBottom())
 51          continue;
 52        else{
 53          if(p.abspid()==4222 || p.abspid()==4212 || p.abspid()==4112)
 54            _h_Sc->fill(p.pT()/GeV);
 55          else if(p.abspid()==4122){
 56            _h_Lc->fill(p.pT()/GeV);
 57            _h_Lc4Ratio->fill(p.pT()/GeV);
 58            if(p.hasAncestorWith(Cuts::pid == 4222) || p.hasAncestorWith(Cuts::pid == 4212) || p.hasAncestorWith(Cuts::pid == 4112) || p.hasAncestorWith(Cuts::pid == -4222) || p.hasAncestorWith(Cuts::pid == -4212) || p.hasAncestorWith(Cuts::pid == -4112))
 59              _h_LcfromSc->fill(p.pT()/GeV);
 60          }
 61          else if(p.abspid()==421){
 62            _h_D0->fill(p.pT()/GeV);
 63            _h_D04Sc->fill(p.pT()/GeV);
 64          }
 65        }
 66      }
 67    }
 68
 69
 70    /// Normalise histograms etc., after the run
 71    void finalize() {
 72
 73      scale(_h_D0,              crossSection()/(microbarn*2*sumOfWeights()));
 74      scale(_h_Lc,              crossSection()/(microbarn*2*sumOfWeights()));
 75      scale(_h_LcfromSc,        crossSection()/(microbarn*2*sumOfWeights()));
 76      scale(_h_Lc4Ratio,        crossSection()/(microbarn*2*sumOfWeights()));
 77      scale(_h_Sc,              crossSection()/(microbarn*2*sumOfWeights()));
 78      scale(_h_D04Sc,           crossSection()/(microbarn*2*sumOfWeights())); // norm to generated cross-section in pb (after cuts)
 79      divide(_h_Sc, _h_D04Sc, _h_ScD0);
 80      divide(_h_Lc, _h_D0, _h_LcD0);
 81      divide(_h_LcfromSc, _h_Lc4Ratio, _h_LcfromScLc);
 82
 83    }
 84
 85    ///@}
 86
 87
 88    /// @name Histograms
 89    ///@{
 90    Histo1DPtr _h_Sc, _h_D0, _h_D04Sc, _h_LcfromSc, _h_Lc , _h_Lc4Ratio;
 91    Estimate1DPtr _h_LcD0, _h_ScD0, _h_LcfromScLc;
 92    ///@}
 93
 94
 95  };
 96
 97
 98  RIVET_DECLARE_PLUGIN(ALICE_2022_I1868463);
 99
100}