main422
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// main422.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
// Analysis
// Proton‑ion
// Centrality
// Angantyr
// This test program will generate p-Pb collisions at sqrt(S_NN)=5TeV
// using Angantyr model for Heavy Ion collisions. The analysis will
// divide the event in centrality classes and measure the charged
// pseudo-rapidity distribution in each class in the way described in
// the ATLAS analysis in arXiv:1508.00848 [hep-ex].
#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 = 2212");
pythia.readString("Beams:idB = 1000822080"); // The lead ion.
pythia.readString("Beams:eA = 4000");
pythia.readString("Beams:eB = 1570");
pythia.readString("Beams:frameType = 2");
// Initialize the Angantyr model to fit the total and semi-includive
// 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 typicall 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 eight centrality bins based on the sum transverse
// emergy in a rapidity interval between -4.9 and -3.2. The borders
// between the classes have been read off the plot in the paper:
double explim[] = {90.0, 66.0, 53.0, 41.0, 32.0, 24.0, 13.0, 6.0};
// Alternatively we can obtain the borders from the generated
// transverse energy spectrum. The default settings should give
// approximately the following:
double genlim[] = {77.5, 54.5, 44.4, 34.1, 27.6, 22.5, 14.2, 3.5};
// If you change any parameters these should also be changed.
// The upper edge of the correponding percentiles:
double pclim[] = {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.6, 0.9};
// Book the pseudorapidity histograms and get counter for sum of
// event weights:
typedef map<double,int,std::greater<double> > MapIdx;
MapIdx expetaidx, genetaidx;
vector<Hist*> expetadist(8), genetadist(8);
string expetaname("EtadistCexp"), genetaname("EtadistCgen");
vector<double> expsumw(8, 0.0), gensumw(8, 0.0);
for ( int i = 0; i < 8; ++i ) {
expetaidx[explim[i]] = i;
expetadist[i] = new Hist(expetaname + char('0' + i), 54, -2.7, 2.7);
genetaidx[genlim[i]] = i;
genetadist[i] = new Hist(genetaname + char('0' + i), 54, -2.7, 2.7);
}
// Book histogram for the centrality measure.
Hist sumet("SumETfwd", 100, 0.0, 200.0);
// 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", 60, -0.5, 59.5);
// Sum up the weights of all generated events.
double sumw = 0.0;
// If Pythia fails to initialize, exit with error.
if (!pythia.init()) return 1;
// Loop over events.
int nEvents = 1000;
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;
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 && eta < -3.2 && eta > -4.9 )
etfwd += p.eT();
}
}
// 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();
wounded.fill(nw, weight);
// Find the correct centrality histograms.
MapIdx::iterator expit = expetaidx.upper_bound(etfwd);
int expidx = expit== expetaidx.end()? -1: expit->second;
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 ( expidx < 0 && genidx < 0 ) continue;
if ( expidx >= 0 ) expsumw[expidx] += weight;
if ( genidx >= 0 ) gensumw[genidx] += 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 ) {
if ( expidx >= 0 ) expetadist[expidx]->fill(p.eta(), weight);
if ( genidx >= 0 ) genetadist[genidx]->fill(p.eta(), weight);
}
}
}
// The run is over, so we write out some statistics.
pythia.stat();
// 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("main422.dat");
sumet /= sumw*2.0;
ofs << "# " << sumet.getTitle() << endl;
sumet.table(ofs);
wounded /= sumw;
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 < 8; ++idx ) {
*expetadist[idx] /= expsumw[idx]*0.1;
ofs << "\n# " << expetadist[idx]->getTitle() << endl;
expetadist[idx]->table(ofs);
delete expetadist[idx];
*genetadist[idx] /= gensumw[idx]*0.1;
ofs << "\n# " << genetadist[idx]->getTitle() << endl;
genetadist[idx]->table(ofs);
delete genetadist[idx];
}
// Befor we end, we want to check that our generated centrality
// classes were the same as we guessed.
double curr = 0.0;
double prev = 0.0;
double acc = 0.0;
int idxa = 7;
double lim = sumw*(1.0 - pclim[idxa]);
vector<double> newlim(8);
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 data\n";
for ( int idx = 0; idx < 8; ++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]
<< setw(10) << fixed << setprecision(1) << explim[idx] << endl;
// And we're done!
return 0;
}