CbmKresSelectAnn.cxx

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/* Copyright (C) 2017-2020 GSI Helmholtzzentrum fuer Schwerionenforschung, Darmstadt
   SPDX-License-Identifier: GPL-3.0-only
   Authors: Ievgenii Kres, Florian Uhlig [committer] */
 
#include "CbmKresSelectAnn.h"
 
#include "TMath.h"
#include "TSystem.h"
#include "TTree.h"
 
#include <boost/assign/list_of.hpp>
 
#include <iostream>
#include <string>
#include <vector>
 
 
using namespace std;
 
CbmKresSelectAnn::CbmKresSelectAnn() : fAnnWeights(), fNN(nullptr) {}
 
CbmKresSelectAnn::~CbmKresSelectAnn() {}
 
void CbmKresSelectAnn::Init()
{
  TTree* simu = new TTree("MonteCarlo", "MontecarloData");
  Double_t x[6];
  Double_t xOut;
 
  simu->Branch("x0", &x[0], "x0/D");
  simu->Branch("x1", &x[1], "x1/D");
  simu->Branch("x2", &x[2], "x2/D");
  simu->Branch("x3", &x[3], "x3/D");
  simu->Branch("x4", &x[4], "x4/D");
  simu->Branch("x5", &x[5], "x5/D");
  simu->Branch("xOut", &xOut, "xOut/D");
 
  fNN = new TMultiLayerPerceptron("x0,x1,x2,x3,x4,x5:12:xOut", simu);
 
  fAnnWeights = string(gSystem->Getenv("VMCWORKDIR")) + "/analysis/conversion2/KresAnalysis_ann_weights.txt";
  cout << "-I- CbmKresSelectAnn: get ANN weight parameters from: " << fAnnWeights << endl;
  fNN->LoadWeights(fAnnWeights.c_str());
}
 
double CbmKresSelectAnn::DoSelect(double InvariantMass, double OpeningAngle, double PlaneAngle_last, double ZPos,
                                  TVector3 Momentum1, TVector3 Momentum2)
{
  double AnnValue = 0;
 
  double p1 =
    TMath::Sqrt(Momentum1.X() * Momentum1.X() + Momentum1.Y() * Momentum1.Y() + Momentum1.Z() * Momentum1.Z());
  double p2 =
    TMath::Sqrt(Momentum2.X() * Momentum2.X() + Momentum2.Y() * Momentum2.Y() + Momentum2.Z() * Momentum2.Z());
 
  if (InvariantMass > 0.5 || OpeningAngle > 45 || ZPos > 100 || p1 > 10 || p2 > 10) { return AnnValue = -1; }
 
 
  double params[6];
 
  params[0] = InvariantMass / 0.1;
  params[1] = OpeningAngle / 30;
  params[2] = PlaneAngle_last / 30;
  params[3] = ZPos / 100;
  params[4] = p1 / 5;
  params[5] = p2 / 5;
 
  if (params[0] > 1.0) params[0] = 1.0;
  if (params[1] > 1.0) params[1] = 1.0;
  if (params[2] > 1.0) params[2] = 1.0;
  if (params[3] > 1.0) params[3] = 1.0;
  if (params[4] > 1.0) params[4] = 1.0;
  if (params[5] > 1.0) params[5] = 1.0;
 
  AnnValue = fNN->Evaluate(0, params);
 
  return AnnValue;
}