Rivet analyses
Anisotropic flow of charged particles in PbPb collisions at 5.02 TeV.
Experiment: ALICE (LHC)
Inspire ID: 1419244
Status: UNVALIDATED
Authors: - Christian Bierlich
References: - Phys.Rev.Lett.116(2016)132302,2016 - DOI:10.1103/PhysRevLett.116.132302 - arXiv: 1602.01119
Beams: 1000822080 1000822080
Beam energies: (522392.0, 522392.0)GeV
Run details: - PbPb minimum bias events.
Anisotropic flow of charged particles in PbPb collisions at 5.02 TeV. The analysis calculates flow coefficients using Generic Framework, integrated and differential in pT, as function of centrality. Only results for 5.02 TeV are calculated in the analysis, even though the HepData record also includes results for 2.76 TeV. Those results should be validated through a separate analysis referring to the primary citation. For this reason also ratios between flow coefficients at two energies are excluded.
Source
code:ALICE_2016_I1419244.cc
// -*- C++ -*-
#include "Rivet/Analysis.hh"
#include "Rivet/Projections/ChargedFinalState.hh"
#include "Rivet/Tools/Correlators.hh"
#include "Rivet/Analyses/AliceCommon.hh"
namespace Rivet {
/// @brief Multiparticle azimuthal correlations PbPb 5TeV.
class ALICE_2016_I1419244 : public CumulantAnalysis {
public:
/// Constructor
ALICE_2016_I1419244()
: CumulantAnalysis("ALICE_2016_I1419244") {
}
/// @name Analysis methods
/// @{
/// Book histograms and initialise projections before the run
void init() {
// Initialise and register projections
// Declare the trigger projection.
declare<ALICE::V0AndTrigger>(ALICE::V0AndTrigger(),"V0-AND");
// Centrality projection.
declareCentrality(ALICE::V0MMultiplicity(), "ALICE_2015_CENT_PBPB", "V0M","V0M");
// The full central charged final state.
const ChargedFinalState& cfs = ChargedFinalState(Cuts::abseta < 0.8 &&
Cuts::pT > 0.2*GeV && Cuts::pT < 5.0*GeV);
declare(cfs, "CFS");
// The positive eta side used for rapidity gap.
const ChargedFinalState& cfsp = ChargedFinalState(Cuts::eta > 0.5 &&
Cuts::eta < 0.8 && Cuts::pT > 0.2*GeV && Cuts::pT < 5.0*GeV);
declare(cfsp, "CFSP");
// ..negative ditto.
const ChargedFinalState& cfsn = ChargedFinalState(Cuts::eta < -0.5 &&
Cuts::eta > -0.8 && Cuts::pT > 0.2*GeV && Cuts::pT < 5.0*GeV);
declare(cfsn, "CFSN");
// Book histograms before booking the correlators
// to have access to bin edges.
book(h_v22gap, 1, 1, 1, true);
book(h_v24, 1, 1, 2, true);
book(h_v26, 1, 1, 3, true);
book(h_v28, 1, 1, 4, true);
book(h_v32gap, 2, 1, 1, true);
book(h_v42gap, 2, 1, 2, true);
book(h_v22gappT, 8, 1, 1, true);
book(h_v32gappT, 8, 1, 2, true);
book(h_v42gappT, 8, 1, 3, true);
book(h_v24pT10, 9, 1, 1, true);
book(h_v24pT20, 9, 1, 2, true);
book(h_v24pT30, 9, 1, 3, true);
book(h_c22gap, "_c22gap", refData(1, 1, 1).mkScatter());
book(h_c24, "_c24", refData(1, 1, 2).mkScatter());
book(h_c26, "_c26", refData(1, 1, 3).mkScatter());
book(h_c28, "_c28", refData(1, 1, 4).mkScatter());
book(h_c32gap, "_c32gap", refData(8, 1, 2).mkScatter());
book(h_c42gap, "_c24gap", refData(8, 1, 3).mkScatter());
book(h_ec22gap, "_ec22gap", refData(1, 1, 1).mkScatter());
book(h_ec22, "_ec22", refData(1, 1, 2).mkScatter());
book(h_ec24, "_ec24", refData(1, 1, 2).mkScatter());
book(h_ec26, "_ec26", refData(1, 1, 3).mkScatter());
book(h_ec28, "_ec28", refData(1, 1, 4).mkScatter());
// Corresponding event averaged correlators.
// Integrated, with gap.
ec22gap = bookECorrelatorGap<2,2>("ec22gap",refData(1,1,1));
ec32gap = bookECorrelatorGap<3,2>("ec32gap",refData(2,1,1));
ec42gap = bookECorrelatorGap<4,2>("ec42gap",refData(2,1,2));
// Integrated, no gap.
ec22 = bookECorrelator<2,2>("ec22",refData(1,1,2));
ec24 = bookECorrelator<2,4>("ec24",refData(1,1,2));
ec26 = bookECorrelator<2,6>("ec26",refData(1,1,3));
ec28 = bookECorrelator<2,8>("ec28",refData(1,1,4));
// pT differential, no gap, three centralities.
ec22pT10 = bookECorrelator<2,2>("ec22pT10",refData(9,1,1));
ec24pT10 = bookECorrelator<2,4>("ec24pT10",refData(9,1,1));
ec22pT20 = bookECorrelator<2,2>("ec22pT20",refData(9,1,2));
ec24pT20 = bookECorrelator<2,4>("ec24pT20",refData(9,1,2));
ec22pT30 = bookECorrelator<2,2>("ec22pT30",refData(9,1,3));
ec24pT30 = bookECorrelator<2,4>("ec24pT30",refData(9,1,3));
// pT differential, with gap, 30-40% centrality.
ec22gappT = bookECorrelatorGap<2,2>("ec22gappT",refData(8,1,1));
ec32gappT = bookECorrelatorGap<3,2>("ec32gappT",refData(8,1,2));
ec42gappT = bookECorrelatorGap<4,2>("ec42gappT",refData(8,1,3));
pair<int, int> max = getMaxValues();
// Declare correlator projections.
declare(Correlators(cfs, max.first, max.second, refData(8,1,1)),
"Correlators");
declare(Correlators(cfsp, max.first, max.second, refData(8,1,1)),
"CorrelatorsPos");
declare(Correlators(cfsn, max.first, max.second, refData(8,1,1)),
"CorrelatorsNeg");
}
/// Perform the per-event analysis
void analyze(const Event& event) {
// Event trigger.
if (!apply<ALICE::V0AndTrigger>(event, "V0-AND")() ) vetoEvent;
// The centrality projection.
const CentralityProjection& centProj =
apply<CentralityProjection>(event,"V0M");
// The centrality.
const double cent = centProj();
// The correlators projections.
const Correlators& c = apply<Correlators>(event,"Correlators");
const Correlators& cp =
apply<Correlators>(event,"CorrelatorsPos");
const Correlators& cn =
apply<Correlators>(event,"CorrelatorsNeg");
ec22gap->fill(cent, cp, cn);
ec32gap->fill(cent, cp, cn);
ec42gap->fill(cent, cp, cn);
ec22->fill(cent, c);
ec24->fill(cent, c);
ec26->fill(cent, c);
ec28->fill(cent, c);
if (cent < 10.) ec22pT10->fill(c), ec24pT10->fill(c);
else if (cent < 20.) ec22pT20->fill(c), ec24pT20->fill(c);
else if (cent < 30.) ec22pT30->fill(c), ec24pT30->fill(c);
else if (cent < 40.) {
ec22gappT->fill(cp, cn);
ec32gappT->fill(cp, cn);
ec42gappT->fill(cp, cn);
}
}
/// Normalise histograms etc., after the run
void finalize() {
// Filling test histos.
cnTwoInt(h_c22gap, ec22gap);
cnTwoInt(h_c32gap, ec32gap);
cnTwoInt(h_c42gap, ec42gap);
cnFourInt(h_c24, ec22, ec24);
cnSixInt(h_c26, ec22, ec24, ec26);
cnEightInt(h_c28, ec22, ec24, ec26, ec28);
corrPlot(h_ec22gap, ec22gap);
corrPlot(h_ec22, ec22);
corrPlot(h_ec24, ec24);
corrPlot(h_ec26, ec26);
corrPlot(h_ec28, ec28);
vnTwoInt(h_v22gap, ec22gap);
vnTwoInt(h_v32gap, ec32gap);
vnTwoInt(h_v42gap, ec42gap);
vnFourInt(h_v24, ec22, ec24);
vnSixInt(h_v26, ec22, ec24, ec26);
vnEightInt(h_v28, ec22, ec24, ec26, ec28);
vnTwoDiff(h_v22gappT, ec22gappT);
vnTwoDiff(h_v32gappT, ec32gappT);
vnTwoDiff(h_v42gappT, ec42gappT);
vnFourDiff(h_v24pT10, ec22pT10, ec24pT10);
vnFourDiff(h_v24pT20, ec22pT20, ec24pT20);
vnFourDiff(h_v24pT30, ec22pT30, ec24pT30);
}
/// @}
/// @name Histograms
/// @{
// The integrated centrality dependent v2{n}.
Scatter2DPtr h_v22gap;
Scatter2DPtr h_v24;
Scatter2DPtr h_v26;
Scatter2DPtr h_v28;
// The integrated, centrality dependent v3,4{2} gapped.
Scatter2DPtr h_v32gap;
Scatter2DPtr h_v42gap;
// The pT differential, v2{2} gapped for 30-40% centrality
Scatter2DPtr h_v22gappT;
// ...v3{2} ditto.
Scatter2DPtr h_v32gappT;
// ... v4{2} ditto.
Scatter2DPtr h_v42gappT;
// The pT differential, centrality dependent v2{4}
Scatter2DPtr h_v24pT10;
Scatter2DPtr h_v24pT20;
Scatter2DPtr h_v24pT30;
// Test histograms -- cumulants
Scatter2DPtr h_c22gap;
Scatter2DPtr h_c24;
Scatter2DPtr h_c26;
Scatter2DPtr h_c28;
Scatter2DPtr h_c32gap;
Scatter2DPtr h_c42gap;
// Test histograms -- correlators.
Scatter2DPtr h_ec22gap;
Scatter2DPtr h_ec22;
Scatter2DPtr h_ec24;
Scatter2DPtr h_ec26;
Scatter2DPtr h_ec28;
// The all event averaged correlators.
// Integrated with gap.
ECorrPtr ec22gap;
ECorrPtr ec32gap;
ECorrPtr ec42gap;
// Integrated without gap.
ECorrPtr ec22;
ECorrPtr ec24;
ECorrPtr ec26;
ECorrPtr ec28;
// pT dependent, 2 particle, gapped, 30-40% centrality
ECorrPtr ec22gappT;
ECorrPtr ec32gappT;
ECorrPtr ec42gappT;
// pT dependent, 4 particle, three centralities.
ECorrPtr ec22pT10;
ECorrPtr ec24pT10;
ECorrPtr ec22pT20;
ECorrPtr ec24pT20;
ECorrPtr ec22pT30;
ECorrPtr ec24pT30;
/// @}
};
RIVET_DECLARE_PLUGIN(ALICE_2016_I1419244);
}