[PWGHF] Selection of prompt and non-prompt scores within a defined range (#11510)

Author: Simple-Shyam

PWGHF/HFC/Tasks/taskCorrelationDplusHadrons.cxx

@@ -80,7 +80,8 @@ struct HfTaskCorrelationDplusHadrons {
   Configurable<int> selectionFlagDplus{"selectionFlagDplus", 7, "Selection Flag for D+"}; // 7 corresponds to topo+PID cuts
   Configurable<bool> selNoSameBunchPileUpColl{"selNoSameBunchPileUpColl", true, "Flag for rejecting the collisions associated with the same bunch crossing"};
   Configurable<std::vector<int>> classMl{"classMl", {0, 1, 2}, "Indexes of ML scores to be stored. Three indexes max."};
-  Configurable<std::vector<double>> mlScorePromptOrNonPrompt{"mlScorePromptOrNonPrompt", {0.5, 0.5, 0.5, 0.5}, "Machine learning scores for prompt or Feed-down"};
+  Configurable<std::vector<double>> mlScorePromptOrNonPromptMin{"mlScorePromptOrNonPromptMin", {0.5, 0.5, 0.5, 0.5}, "Minimum Machine learning scores for prompt or Feed-down"};
+  Configurable<std::vector<double>> mlScorePromptOrNonPromptMax{"mlScorePromptOrNonPromptMax", {1.0, 1.0, 1.0, 1.0}, "Maximum Machine learning scores for prompt or Feed-down"};
   Configurable<std::vector<double>> mlScoreBkg{"mlScoreBkg", {0.5, 0.5, 0.5, 0.5}, "Machine learning scores for bkg"};
   // pT ranges for correlation plots: the default values are those embedded in hf_cuts_dplus_to_pi_k_pi (i.e. the mass pT bins), but can be redefined via json files
   Configurable<std::vector<double>> binsPtCorrelations{"binsPtCorrelations", std::vector<double>{ptBinsCorrelationsVec}, "pT bin limits for correlation plots"};
@@ -315,7 +316,7 @@ struct HfTaskCorrelationDplusHadrons {
         continue;
       }
 
-      if (bdtScorePromptOrNonPrompt < mlScorePromptOrNonPrompt->at(effBinD) || bdtScoreBkg > mlScoreBkg->at(effBinD)) {
+      if (bdtScorePromptOrNonPrompt < mlScorePromptOrNonPromptMin->at(effBinD) || bdtScorePromptOrNonPrompt > mlScorePromptOrNonPromptMax->at(effBinD) || bdtScoreBkg > mlScoreBkg->at(effBinD)) {
         continue;
       }
       double efficiencyWeightD = 1.;
@@ -354,7 +355,7 @@ struct HfTaskCorrelationDplusHadrons {
         continue;
       }
 
-      if (bdtScorePromptOrNonPrompt < mlScorePromptOrNonPrompt->at(effBinD) || bdtScoreBkg > mlScoreBkg->at(effBinD)) {
+      if (bdtScorePromptOrNonPrompt < mlScorePromptOrNonPromptMin->at(effBinD) || bdtScorePromptOrNonPrompt > mlScorePromptOrNonPromptMax->at(effBinD) || bdtScoreBkg > mlScoreBkg->at(effBinD)) {
         continue;
       }
       if (trackDcaXY > dcaXYTrackMax || trackDcaZ > dcaZTrackMax || trackTpcCrossedRows < nTpcCrossedRaws) {
@@ -417,7 +418,7 @@ struct HfTaskCorrelationDplusHadrons {
       if (ptD < binsPtEfficiencyD->front() || ptD > binsPtEfficiencyD->back())
         continue;
 
-      if (bdtScorePromptOrNonPrompt < mlScorePromptOrNonPrompt->at(effBinD) || bdtScoreBkg > mlScoreBkg->at(effBinD)) {
+      if (bdtScorePromptOrNonPrompt < mlScorePromptOrNonPromptMin->at(effBinD) || bdtScorePromptOrNonPrompt > mlScorePromptOrNonPromptMax->at(effBinD) || bdtScoreBkg > mlScoreBkg->at(effBinD)) {
         continue;
       }
       double efficiencyWeightD = 1.;
@@ -471,7 +472,7 @@ struct HfTaskCorrelationDplusHadrons {
       if (ptD < binsPtEfficiencyD->front() || ptD > binsPtEfficiencyD->back())
         continue;
 
-      if (bdtScorePromptOrNonPrompt < mlScorePromptOrNonPrompt->at(effBinD) || bdtScoreBkg > mlScoreBkg->at(effBinD)) {
+      if (bdtScorePromptOrNonPrompt < mlScorePromptOrNonPromptMin->at(effBinD) || bdtScorePromptOrNonPrompt > mlScorePromptOrNonPromptMax->at(effBinD) || bdtScoreBkg > mlScoreBkg->at(effBinD)) {
         continue;
       }
       if (trackDcaXY > dcaXYTrackMax || trackDcaZ > dcaZTrackMax || trackTpcCrossedRows < nTpcCrossedRaws) {
@@ -628,7 +629,7 @@ struct HfTaskCorrelationDplusHadrons {
       for (unsigned int iclass = 0; iclass < classMl->size(); iclass++) {
         outputMl[iclass] = candidate.mlProbDplusToPiKPi()[classMl->at(iclass)];
       }
-      if (outputMl[0] > mlScoreBkg->at(o2::analysis::findBin(binsPtEfficiencyD, candidate.pt())) || outputMl[idxBdtScore] < mlScorePromptOrNonPrompt->at(o2::analysis::findBin(binsPtEfficiencyD, candidate.pt()))) {
+      if (outputMl[0] > mlScoreBkg->at(o2::analysis::findBin(binsPtEfficiencyD, candidate.pt())) || outputMl[idxBdtScore] < mlScorePromptOrNonPromptMin->at(o2::analysis::findBin(binsPtEfficiencyD, candidate.pt())) || outputMl[idxBdtScore] > mlScorePromptOrNonPromptMax->at(o2::analysis::findBin(binsPtEfficiencyD, candidate.pt()))) {
         continue;
       }
       auto collision = candidate.template collision_as<soa::Join<aod::Collisions, aod::FT0Mults, aod::EvSels>>();