## main369

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    // main369.cc is a part of the PYTHIA event generator.
    // Copyright (C) 2026 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:
    //            Torbjorn Sjostrand

    // Keywords:
    //            Top
    //            Toponium
    //            Openmp

    // Plot ttbar system properties contrasting five different scenarios:
    // 0 : pure Born baseline.
    // 1 : Coulomb enhancement.
    // 2 : Green's function, full top width in Breit-Wigner and matrix element.
    // 3 :  Green's function, reduced top width in Breit-Wigner.
    // 4 :  Green's function, reduced top width in matrix elements.
    // Plots all scenarios in one frame, which may be too much.

    // Runs in parallel if Pythia configured with --with-openmp.
    // The same analysis is also provided with main370 but using PythiaParallel.

    // Based on threshold factors according to
    // V. Fadin,  V. Khoze and T. Sjostrand, Z. Phys. C48 (1990) 613.

    #include "Pythia8/Pythia.h"
    using namespace Pythia8;

    // OpenMP includes an user-specifiable number of threads requested.
    // (Will be capped by system maximum. Use -1 to use all available.)
    #ifdef OPENMP
    #include <omp.h>
    const int nThreadsRequested = -1;
    #endif

    //============================================================================

    int main() {

      // Restrict to threshold region only or not.
      bool thresholdOnly   = true;

      // Full event generation or only cross-section-oriented studies.
      bool fullEvents      = false;

      // Number of events.
      int nEvent           = 1000000;

      // LHC collision energy.
      double eCM           = 13000.;

      // Example of common possible setup values.
      double mt            = 172.5;
      double thrRegion     = 10.;
      int    alphasOrder   = 2;
      double alphasValue   = 0.118;
      double ggSingletFrac = 2. / 7.;
      double qqSingletFrac = 0.;

      // Histograms.
      Hist   mHatLow[5], mHatLowZoom[5], mThrOrig[5], mThrOrigZoom[5];
      for (int scenario = 0; scenario < 5; ++scenario) {
        mHatLow[scenario].book("ttbar invariant mass, low", 100, 300., 400.);
        mHatLowZoom[scenario].book("ttbar invariant mass, low", 100, 330., 355.);
        mThrOrig[scenario].book("original threshold", 100, -20., 80.);
        mThrOrigZoom[scenario].book("original threshold, zoom", 100, -10., 30.);
      }

      // Compare the five scenarios.
      // Automatically run in parallel if Pythia configured with flag
      // --with-openmp.
    #ifdef OPENMP
      int nThreadsSys = omp_get_max_threads();
      int nThreadsMax = min(5, nThreadsSys);
      int nThreads    = nThreadsRequested;
      if (nThreads <= 0 || nThreads > nThreadsMax) nThreads = nThreadsMax;
      cout << " OMP parralelisation using "<<nThreads << " threads."
           << " System maximum = "<<nThreadsSys<<"." << endl;
    #pragma omp parallel for num_threads(nThreads)
    #endif
      for (int scenario = 0; scenario < 5; ++scenario) {

        // Scenario-specific settings choices.
        int    topModel    = min( scenario, 2);
        double gammat      = (scenario != 3) ? 1.34 : 0.2;
        // Warning:  a small gammatGreen may lead to event weights above unity.
        double gammatGreen = (scenario != 4) ? 1.34 : 0.2;

        // Generator.
        Pythia pythia;

        // Feed in desired values.
        pythia.settings.mode("TopThreshold:model", topModel);
        pythia.particleData.m0(6, mt);
        pythia.readString("6:doForceWidth = true");
        pythia.particleData.mWidth( 6, gammat);
        pythia.settings.parm("TopThreshold:tWidthGreen",   gammatGreen);
        pythia.settings.parm("TopThreshold:thrRegion",     thrRegion);
        pythia.settings.mode("TopThreshold:alphasOrder",   alphasOrder);
        pythia.settings.parm("TopThreshold:alphasValue",   alphasValue);
        pythia.settings.parm("TopThreshold:ggSingletFrac", ggSingletFrac);
        pythia.settings.parm("TopThreshold:qqSingletFrac", qqSingletFrac);

        // Process selection and collision energy.
        pythia.readString("Top:gg2ttbar = on");
        pythia.readString("Top:qqbar2ttbar = on");
        pythia.settings.parm("Beams:eCM", eCM);

        // Restrict to threshold region.
        if (thresholdOnly) {
          pythia.readString("PhaseSpace:mHatMin = 300.");
          pythia.readString("PhaseSpace:mHatMax = 400.");
        }
        else pythia.readString("PhaseSpace:mHatMin = 200.");

        // Switch off (most) code parts not relevant here. Reduce printout.
        if (!fullEvents) {
          pythia.readString("PartonLevel:ISR = off");
          pythia.readString("PartonLevel:FSR = off");
          pythia.readString("PartonLevel:MPI = off");
          pythia.readString("HadronLevel:all = off");
        }
        pythia.readString("Next:numberCount = 100000");

        // If Pythia fails to initialize, abort this scenario.
        // Use critical to avoid thread clashes when initializing in parallel.
        bool ok = true;
    #ifdef OPENMP
    #pragma omp critical
    #endif
        {
          cout << " =============================================================="
            "=========\n Initializing PYTHIA for scenario " << scenario
               << " with topModel " << topModel << " and top mass " << mt
               << " GeV" << endl;
          ok = pythia.init();
          cout << " Completed initialization of PYTHIA for scenario " << scenario
               << "\n ============================================================"
            "===========\n";
        }
        if (!ok) continue;

        // Counters. Real numbers since some events can come with weights > 1.
        double nEventEff = 0., nBelow2mt = 0., nBelow = 0., nBelowgg = 0.,
          nBelowqq = 0., nAbovegg = 0., nAboveqq = 0., eBelow = 0.;

        // Begin event loop.
        for (int iEvent = 0; iEvent < nEvent; ++iEvent) {

          // Generate events.
          if (!pythia.next()) continue;

          // Original threshold value and other properties.
          double eThr = pythia.info.toponiumE;
          double mHat = pythia.info.mHat();
          double wt   = pythia.info.weight();

          // Histogram threshold value and ttbar invariant mass.
          // Use critical to avoid thread clashes when filling histogram
          // in parallel.
    #ifdef OPENMP
    #pragma omp critical
    #endif
          {
            mHatLow[scenario].fill( mHat, wt);
            mHatLowZoom[scenario].fill( mHat, wt);
            mThrOrig[scenario].fill( eThr, wt);
            mThrOrigZoom[scenario].fill( eThr, wt);
          }

          // Statistics for below- and above-threshold cross sections.
          nEventEff += wt;
          if (mHat < 2. * mt) nBelow2mt += wt;
          if (eThr < 0.) {
            nBelow += wt;
            if (pythia.info.code() == 601) nBelowgg += wt;
            else nBelowqq += wt;
            eBelow += eThr * wt;
          } else {
            if (pythia.info.code() == 601) nAbovegg += wt;
            else nAboveqq += wt;
          }

        // End of event loop.
        }

        // Normalization and statistics for this scenario.
        // Use critical to avoid thread clashes.
    #ifdef OPENMP
    #pragma omp critical
    #endif
        {
          cout << "\n ===== End-of-run statistics for scenario " << scenario
               << " with topModel " << topModel << " and top mass " << mt
               << " GeV =====" << endl;

          // Normalize histogram to cross section, in pb/GeV.
          double sigmaGen         = 1e9 * pythia.info.sigmaGen();
          double sigmapb          = sigmaGen / nEventEff;
          mHatLow[scenario]      *= sigmapb;
          mHatLowZoom[scenario]  *= 4. * sigmapb;
          mThrOrig[scenario]     *= sigmapb;
          mThrOrigZoom[scenario] *= 2.5 * sigmapb;

          // Statistics and cross sections.
          pythia.stat();
          double sigma2mt = sigmapb * nBelow2mt;
          double sigAbv   = sigmapb * (nEventEff - nBelow);
          double sigAbvgg = sigmapb * nAbovegg;
          double sigAbvqq = sigmapb * nAboveqq;
          double sigBel   = sigmapb * nBelow;
          double sigBelgg = sigmapb * nBelowgg;
          double sigBelqq = sigmapb * nBelowqq;
          cout << "\n total sigma in studied region = " << fixed << setprecision(3)
               << sigmaGen << " pb whereof below 2 mt = " << sigma2mt << " pb"
               << endl << " sigma above threshold = "  << sigAbv << " pb" << endl
               << "   whereof gg = " << sigAbvgg << " pb and qq = " << sigAbvqq
               << " pb" << endl << " sigma below threshold = " << sigBel
               << " pb" << endl << "   whereof gg = "
               << sigBelgg << " pb and qq = " << sigBelqq << " pb" << endl;
          eBelow /= max(1., nBelow);
          cout << " average energy for below-threshold part = " << eBelow
               << " GeV" << endl;
        }

      // End loop over threshold cases.
      }

      // No more parallelism beyond this point.
    #ifdef OPENMP
    #pragma omp barrier
    #endif

      // Histograms with pyplot. Common notation.
      HistPlot hpl("plot369");
      string symbol[5] = {"h,black", "h,olive", "h,red", "h,orange", "h,blue"};
      string legend[5] = {"Born", "Coulomb", "Green's, wide BW and ME",
          "Green's, narrow BW", "Green's, narrow ME"};

      // Invariant mass of the ttbar pair.
      hpl.frame("fig369", "invariant mass of ttbar pair, near threshold",
        "$m(t+tbar) (GeV)$", "$\\mathrm{d}\\sigma/\\mathrm{d}m$ (pb/GeV)");
      for (int scenario = 0; scenario < 5; ++scenario)
        hpl.add( mHatLow[scenario], symbol[scenario], legend[scenario]);
      hpl.plot();
      hpl.frame("", "invariant mass of ttbar pair, near threshold, zoom-in",
        "$m(t+tbar) (GeV)$", "$\\mathrm{d}\\sigma/\\mathrm{d}m$ (pb/GeV)");
      for (int scenario = 0; scenario < 5; ++scenario)
        hpl.add( mHatLowZoom[scenario], symbol[scenario], legend[scenario]);
      hpl.plot();

      // The formal E_thr value above/below threshold, key for reweighting.
      hpl.frame("", "Energy above or below threshold",
        "$E$ (GeV)", "$\\mathrm{d}\\sigma/\\mathrm{d}m$ (pb/GeV)");
      for (int scenario = 0; scenario < 5; ++scenario)
        hpl.add( mThrOrig[scenario], symbol[scenario], legend[scenario]);
      hpl.plot();
      hpl.frame("", "Energy above or below threshold, zoom-in",
        "$E$ (GeV)", "$\\mathrm{d}\\sigma/\\mathrm{d}m$ (pb/GeV)");
      for (int scenario = 0; scenario < 5; ++scenario)
        hpl.add( mThrOrigZoom[scenario], symbol[scenario], legend[scenario]);
      hpl.plot();

      // Done.
      return 0;
    }