Rivet analyses

title: 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

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

include "Rivet/Analysis.hh"

include "Rivet/Projections/UnstableParticles.hh"

namespace Rivet {

/// @brief Measurement of prompt charm hadrons production in proton-proton Collisions at 13 TeV class ALICE_2022_I1868463 : public Analysis { public:

/// Constructor
RIVET_DEFAULT_ANALYSIS_CTOR(ALICE_2022_I1868463);


/// @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 UnstableParticles up(Cuts::absrap < 0.5);
  declare(up, "up");

  book(_h_D0, 1, 1, 1);
  book(_h_Lc, 2, 1, 1);
  book(_h_Sc, 3, 1, 1);
  book(_h_LcfromSc, 4, 1, 1);
  book(_h_LcD0, 5, 1, 1);
  book(_h_ScD0, 6, 1, 1);
  book(_h_LcfromScLc, 7, 1, 1);
  book(_h_D04Sc, "TMP/D04Sc", refData(3, 1, 1));
  book(_h_Lc4Ratio, "TMP/Lc4Ratio", refData(4, 1, 1));
}


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

  const UnstableParticles& up = apply<UnstableParticles>(event, "up");

  for (const Particle& p : up.particles()) {
    if (p.fromBottom())
      continue;
    else {
      if (p.abspid() == 4222 || p.abspid() == 4212 || p.abspid() == 4112)
        _h_Sc->fill(p.pT() / GeV);
      else if (p.abspid() == 4122) {
        _h_Lc->fill(p.pT() / GeV);
        _h_Lc4Ratio->fill(p.pT() / GeV);
        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))
          _h_LcfromSc->fill(p.pT() / GeV);
      }
      else if (p.abspid() == 421) {
        _h_D0->fill(p.pT() / GeV);
        _h_D04Sc->fill(p.pT() / GeV);
      }
    }
  }
}


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

  scale(_h_D0, crossSection() / (microbarn * 2 * sumOfWeights()));
  scale(_h_Lc, crossSection() / (microbarn * 2 * sumOfWeights()));
  scale(_h_LcfromSc, crossSection() / (microbarn * 2 * sumOfWeights()));
  scale(_h_Lc4Ratio, crossSection() / (microbarn * 2 * sumOfWeights()));
  scale(_h_Sc, crossSection() / (microbarn * 2 * sumOfWeights()));
  scale(_h_D04Sc,
        crossSection()
            / (microbarn * 2 * sumOfWeights())); // norm to generated cross-section in pb (after cuts)
  divide(_h_Sc, _h_D04Sc, _h_ScD0);
  divide(_h_Lc, _h_D0, _h_LcD0);
  divide(_h_LcfromSc, _h_Lc4Ratio, _h_LcfromScLc);
}

///@}


/// @name Histograms
///@{
Histo1DPtr _h_Sc, _h_D0, _h_D04Sc, _h_LcfromSc, _h_Lc, _h_Lc4Ratio;
Estimate1DPtr _h_LcD0, _h_ScD0, _h_LcfromScLc;
///@}

};

RIVET_DECLARE_PLUGIN(ALICE_2022_I1868463);

} ```