Rivet analyses referenceMC_CENT_PPB_ETATemplate analysis for ontaining eta distributions binned in centralityExperiment: () Status: UNVALIDATED Authors:
Beam energies: ANY Run details:
Template analysis for obtaining eta distributions binned in centrality using the CentralityProjection and Percentile<> classes. The example is pPb collisions at 5 TeV and is based on the ATLAS analysis arXiv:1508.00848 [hep-ex]. The reference YODA file contains the corresponding plots from HepData. The generator should be run in minimum-bias mode with a cut on the transverse momentum of charged particles of 0.1 GeV, and setting particles with tcau>10 fm stable. Note that a calibration histogram for the generated centrality may be preloaded with the output of a corresponding MC_Cent_pPb_Calib analysis. Source code: MC_CENT_PPB_ETA.cc 1// -*- C++ -*-
2#include "Rivet/Analysis.hh"
3#include "Rivet/Analyses/MC_CENT_PPB_Projections.hh"
4#include "Rivet/Tools/Percentile.hh"
5
6namespace Rivet {
7
8
9 class MC_CENT_PPB_ETA : public Analysis {
10 public:
11
12 RIVET_DEFAULT_ANALYSIS_CTOR(MC_CENT_PPB_ETA);
13
14 /// Book histograms and initialise projections before the run
15 void init() {
16
17 MSG_INFO("CENT parameter set to " << getOption<string>("cent","REF"));
18
19 // The centrality projection.
20 declareCentrality(MC_SumETFwdPbCentrality(),
21 "MC_CENT_PPB_CALIB", "SumETPb", "CENT");
22
23 // The trigger projection.
24 declare(MC_pPbMinBiasTrigger(), "Trigger");
25
26 // The particles to be analysed.
27 declare(ChargedFinalState(Cuts::abseta < 2.7 && Cuts::pT > 0.1*GeV), "CFS");
28
29 // The centrality bins and the corresponding histograms.
30 std::vector< std::pair<double, double> > centralityBins =
31 { {0, 1}, {1, 5}, {5, 10}, {10, 20},
32 {20, 30}, {30, 40}, {40, 60}, {60, 90} };
33 // std::vector< std::tuple<int, int, int> > refData =
34 // { {2, 1, 8}, {2, 1, 7}, {2, 1, 6}, {2, 1, 5},
35 // {2, 1, 4}, {2, 1, 3}, {2, 1, 2}, {2, 1, 1} };
36 std::vector< std::tuple<size_t, size_t, size_t> > refData;
37 refData.reserve(8);
38 for (size_t i = 8; i > 0; --i ) {
39 refData.push_back(std::tuple<size_t, size_t, size_t>(2, 1, i));
40 }
41
42 // The centrality-binned histograms.
43 _hEta = book<Histo1D>("CENT", centralityBins, refData);
44
45 }
46
47
48 /// Perform the per-event analysis
49 void analyze(const Event& event) {
50
51 if ( !apply<TriggerProjection>(event, "Trigger")() ) vetoEvent;
52
53 _hEta->init(event);
54 for ( const auto &p : apply<ChargedFinalState>(event,"CFS").particles() )
55 _hEta->fill(p.eta());
56 }
57
58
59 /// Finalize
60 void finalize() {
61 // Scale by the inverse sum of event weights in each centrality bin.
62 _hEta->normalizePerEvent();
63 }
64
65
66 private:
67
68 /// The histograms binned in centrality.
69 Percentile<Histo1D> _hEta;
70
71 };
72
73
74 RIVET_DECLARE_PLUGIN(MC_CENT_PPB_ETA);
75
76}
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