main113
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// main113.cc is a part of the PYTHIA event generator.
// Copyright (C) 2024 Torbjorn Sjostrand.
// PYTHIA is licenced under the GNU GPL v2 or later, see COPYING for details.
// Please respect the MCnet Guidelines, see GUIDELINES for details.
// Authors:
// Leif Lonnblad
// Keywords:
// Heavy ions
// Charged multiplicity
// Centrality
// Angantyr
// This test program will generate Pb-Pb collisions at
// sqrt(S_NN)=2.76TeV using the Angantyr model for Heavy Ion
// collisions. The analysis will divide the event in centrality
// classes using the same observable as was used for p-Pb in the ATLAS
// analysis in arXiv:1508.00848 [hep-ex] (see main112.cc). The
// centrality classes are same as in the ALICE analysis in
// arXiv:1012.1657 [nucl-ex] although the actual observable used is
// not the same. Histograms of multiplicity distributions are measured
// for each centrality percentile.
// Note that heavy ion collisions are computationally quite CPU
// intensive and generating a single event will take around a second
// on a reasonable desktop. To get reasonable statistics, this program
// will take a couple of hours to run.
#include "Pythia8/Pythia.h"
// You need to include this to get access to the HIInfo object for
// HeavyIons.
#include "Pythia8/HeavyIons.h"
using namespace Pythia8;
int main() {
Pythia pythia;
// Setup the beams.
pythia.readString("Beams:idA = 1000822080");
pythia.readString("Beams:idB = 1000822080"); // The lead ions.
pythia.readString("Beams:eCM = 2760.0");
pythia.readString("Beams:frameType = 1");
// Initialize the Angantyr model to fit the total and semi-inclusive
// cross sections in Pythia within some tolerance.
pythia.readString("HeavyIon:SigFitErr = "
"0.02,0.02,0.1,0.05,0.05,0.0,0.1,0.0");
// These parameters are typically suitable for sqrt(S_NN)=5TeV
pythia.readString("HeavyIon:SigFitDefPar = 2.15,17.24,0.33");
// A simple genetic algorithm is run for 20 generations to fit the
// parameters.
pythia.readString("HeavyIon:SigFitNGen = 20");
// There will be nine centrality bins based on the sum transverse
// emergy in a rapidity interval between 3.2 and 4.9 obtained from
// the borders from the generated transverse energy spectrum. The
// default settings should give approximately the following:
double genlim[] = {2979.4, 2400.1, 1587.5, 1028.8, 669.9,
397.4, 220.3, 116.3, 54.5};
// If you change any parameters these should also be changed.
// The upper edge of the correponding percentiles:
double pclim[] = {0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8};
// Book the pseudorapidity and multiplicity histograms and get
// counters for number of events and sum of event weights:
typedef map<double,int,std::greater<double> > MapIdx;
MapIdx genetaidx;
vector<Hist*> etadist(9), lmult(9), hmult(9);
string etaname("EtadistC"), mname("MultC");
vector<double> gensumw(9, 0.0), gensumn(9, 0.0);
for ( int i = 0; i < 9; ++i ) {
genetaidx[genlim[i]] = i;
etadist[i] = new Hist(etaname + char('0' + i), 54, -2.7, 2.7);
hmult[i] = new Hist(mname + 'H' + char('0' + i),
75, -0.5, 2999.5);
lmult[i] = new Hist(mname + 'L' + char('0' + i),
75, -0.5, 299.5);
}
// Book histogram for the centrality measure.
Hist sumet("SumETfwd", 200, 0.0, 4000.0);
// Also make a map of all weight to check the generated centrality
// classes.
multimap<double,double> gencent;
// Book a histogram for the distribution of number of wounded
// nucleons.
Hist wounded("Nwounded", 209, -0.5, 417.5);
// Profile for average central multiplicity and number of wounded
// nucleons as a function of centrality (with errors).
vector<double> cmult(9, 0.0), cmult2(9, 0.0);
vector<double> wound(9, 0.0), wound2(9, 0.0);
// Sum up the weights of all generated events.
double sumw = 0.0;
// Initialise Pythia.
pythia.init();
// Loop over events.
int nEvents = 100;
for ( int iEvent = 0; iEvent < nEvents; ++iEvent ) {
if ( !pythia.next() ) continue;
// First sum up transverse energy for centrality measure and also
// check that the trigger requiring ar least one charged particle
// forward and backward.
double etfwd = 0.0;
bool trigfwd = false;
bool trigbwd = false;
int nc = 0;
for (int i = 0; i < pythia.event.size(); ++i) {
Particle & p = pythia.event[i];
if ( p.isFinal() ) {
double eta = p.eta();
if ( p.isCharged() && p.pT() > 0.1 && eta < -2.09 && eta > -3.84 )
trigfwd = true;
if ( p.isCharged() && p.pT() > 0.1 && eta > 2.09 && eta < 3.84 )
trigbwd = true;
if ( p.pT() > 0.1 && abs(eta) > 3.2 && abs(eta) < 4.9 )
etfwd += p.eT();
if ( p.isCharged() && p.pT() > 0.1 && abs(eta) < 0.5 ) ++nc;
}
}
// Skip if not triggered
if ( !(trigfwd && trigbwd) ) continue;
// Keep track of the sum of waights
double weight = pythia.info.weight();
sumw += weight;
// Histogram and save the summed Et.
sumet.fill(etfwd, weight);
gencent.insert(make_pair(etfwd, weight));
// Also fill the number of (absorptively and diffractively)
// wounded nucleaons.
int nw = pythia.info.hiInfo->nAbsTarg() +
pythia.info.hiInfo->nDiffTarg() +
pythia.info.hiInfo->nAbsProj() +
pythia.info.hiInfo->nDiffProj();
wounded.fill(nw, weight);
// Find the correct centrality histograms.
MapIdx::iterator genit = genetaidx.upper_bound(etfwd);
int genidx = genit== genetaidx.end()? -1: genit->second;
// Sum the weights in the centrality classes, skip if not in a class.
if ( genidx < 0 ) continue;
gensumw[genidx] += weight;
hmult[genidx]->fill(nc, weight);
lmult[genidx]->fill(nc, weight);
gensumn[genidx] += 1.0;
cmult[genidx] += nc*weight;
cmult2[genidx] += nc*nc*weight;
wound[genidx] += nw*weight;
wound2[genidx] += nw*nw*weight;
// Go through the event again and fill the eta distributions.
for (int i = 0; i < pythia.event.size(); ++i) {
Particle & p = pythia.event[i];
if ( p.isFinal() && p.isCharged() &&
abs(p.eta()) < 2.7 && p.pT() > 0.1 ) {
etadist[genidx]->fill(p.eta(), weight);
}
}
}
// The run is over, so we write out some statistics.
// Now, we just have to normalize and prtint out the histograms. We
// choose to print the histograms to a file that can be read by
// eg. gnuplot.
ofstream ofs("main113.dat");
sumet /= sumw*2.0;
ofs << "# " << sumet.getTitle() << endl;
sumet.table(ofs);
wounded /= sumw*2.0;
ofs << "\n# " << wounded.getTitle() << endl;
wounded.table(ofs);
// Print out the centrality binned eta distributions and delete the
// heap-allocate histograms.
for ( int idx = 0; idx < 9; ++idx ) {
*hmult[idx] /= gensumw[idx]*40.0;
ofs << "\n# " << hmult[idx]->getTitle() << endl;
hmult[idx]->table(ofs);
delete hmult[idx];
*lmult[idx] /= gensumw[idx]*4.0;
ofs << "\n# " << lmult[idx]->getTitle() << endl;
lmult[idx]->table(ofs);
delete lmult[idx];
*etadist[idx] /= gensumw[idx]*0.1;
ofs << "\n# " << etadist[idx]->getTitle() << endl;
etadist[idx]->table(ofs);
delete etadist[idx];
}
// Print out average central charged multiplicity as a function of
// centrality.
ofs << "\n# Nch0\n";
for ( int idx = 0; idx < 9; ++idx ) {
double Nch = cmult[idx]/gensumw[idx];
cmult2[idx] = (cmult2[idx]/gensumw[idx] - pow2(Nch))/gensumn[idx];
ofs << setprecision(2) << setw(4) << int(pclim[idx]*100.0 + 0.5)
<< setw(10) << Nch << setw(10) << sqrt(cmult2[idx]) <<endl;
}
ofs << "\n# Nwc\n";
for ( int idx = 0; idx < 9; ++idx ) {
double Nw = wound[idx]/gensumw[idx];
wound2[idx] = (wound2[idx]/gensumw[idx] - pow2(Nw))/gensumn[idx];
ofs << setprecision(2) << setw(4) << int(pclim[idx]*100.0 + 0.5)
<< setw(10) << Nw << setw(10) << sqrt(wound2[idx]) <<endl;
}
// Befor we end we print out some statistics. Also, we want to check
// that our generated centrality classes were the same as we
// guessed.
pythia.stat();
double curr = 0.0;
double prev = 0.0;
double acc = 0.0;
int idxa = 8;
double lim = sumw*(1.0 - pclim[idxa]);
vector<double> newlim(9);
for ( multimap<double, double>::iterator it = gencent.begin();
it != gencent.end(); ++it ) {
prev = curr;
curr = it->first;
double w = it->second;
if ( acc < lim && acc + w >= lim ) {
newlim[idxa--] = prev + (curr - prev)*(lim - acc)/w;
if ( idxa < 0 ) break;
lim = sumw*(1.0 - pclim[idxa]);
}
acc += w;
}
cout << "The generated limits between centrality classes in this run:\n"
<< " % assumed actual\n";
for ( int idx = 0; idx < 9; ++idx )
cout << setw(4) << int(pclim[idx]*100.0 + 0.5)
<< setw(10) << fixed << setprecision(1) << genlim[idx]
<< setw(10) << fixed << setprecision(1) << newlim[idx] << endl;
// And we're done!
return 0;
}