#include <TracksterLinkingbySuperClusteringDNN.h>

Inheritance diagram for ticl::TracksterLinkingbySuperClusteringDNN:

Public Member Functions
void	initialize (const HGCalDDDConstants *hgcons, const hgcal::RecHitTools rhtools, const edm::ESHandle< MagneticField > bfieldH, const edm::ESHandle< Propagator > propH) override

void	linkTracksters (const Inputs &input, std::vector< Trackster > &resultTracksters, std::vector< std::vector< unsigned int >> &linkedResultTracksters, std::vector< std::vector< unsigned int >> &linkedTracksterIdToInputTracksterId) override

	TracksterLinkingbySuperClusteringDNN (const edm::ParameterSet &ps, edm::ConsumesCollector iC, cms::Ort::ONNXRuntime const *onnxRuntime=nullptr)

	~TracksterLinkingbySuperClusteringDNN () override

Public Member Functions inherited from ticl::TracksterLinkingAlgoBase
virtual void	setEvent (edm::Event &iEvent, edm::EventSetup const &iEventSetup)

	TracksterLinkingAlgoBase (const edm::ParameterSet &conf, edm::ConsumesCollector, cms::Ort::ONNXRuntime const *onnxRuntime=nullptr)

virtual	~TracksterLinkingAlgoBase ()

Static Public Member Functions
static void	fillPSetDescription (edm::ParameterSetDescription &iDesc)

Static Public Member Functions inherited from ticl::TracksterLinkingAlgoBase
static void	fillPSetDescription (edm::ParameterSetDescription &desc)

Private Member Functions
bool	checkExplainedVarianceRatioCut (ticl::Trackster const &ts) const

bool	trackstersPassesPIDCut (const Trackster &ts) const

Private Attributes
float	candidateEnergyThreshold_

float	deltaEtaWindow_

float	deltaPhiWindow_

std::unique_ptr< AbstractSuperclusteringDNNInput >	dnnInputs_

float	explVarRatioCut_energyBoundary_

float	explVarRatioMinimum_highEnergy_

float	explVarRatioMinimum_lowEnergy_

bool	filterByTracksterPID_

unsigned int	inferenceBatchSize_

double	nnWorkingPoint_

float	PIDThreshold_

float	seedPtThreshold_

std::vector< int >	tracksterPIDCategoriesToFilter_

Additional Inherited Members
Protected Attributes inherited from ticl::TracksterLinkingAlgoBase
int	algo_verbosity_

cms::Ort::ONNXRuntime const *	onnxRuntime_

Detailed Description

Definition at line 30 of file TracksterLinkingbySuperClusteringDNN.h.

Constructor & Destructor Documentation

◆ TracksterLinkingbySuperClusteringDNN()

TracksterLinkingbySuperClusteringDNN::TracksterLinkingbySuperClusteringDNN	(	const edm::ParameterSet &	ps,
		edm::ConsumesCollector	iC,
		cms::Ort::ONNXRuntime const *	onnxRuntime = `nullptr`
	)

Definition at line 41 of file TracksterLinkingbySuperClusteringDNN.cc.

References cms::cuda::assert(), and ticl::TracksterLinkingAlgoBase::onnxRuntime_.

     : TracksterLinkingAlgoBase(ps, iC, onnxRuntime),
       dnnInputs_(makeSuperclusteringDNNInputFromString(ps.getParameter<std::string>("dnnInputsVersion"))),
       inferenceBatchSize_(ps.getParameter<unsigned int>("inferenceBatchSize")),
       nnWorkingPoint_(ps.getParameter<double>("nnWorkingPoint")),
       deltaEtaWindow_(ps.getParameter<double>("deltaEtaWindow")),
       deltaPhiWindow_(ps.getParameter<double>("deltaPhiWindow")),
       seedPtThreshold_(ps.getParameter<double>("seedPtThreshold")),
       candidateEnergyThreshold_(ps.getParameter<double>("candidateEnergyThreshold")),
       explVarRatioCut_energyBoundary_(ps.getParameter<double>("candidateEnergyThreshold")),
       explVarRatioMinimum_lowEnergy_(ps.getParameter<double>("explVarRatioMinimum_lowEnergy")),
       explVarRatioMinimum_highEnergy_(ps.getParameter<double>("explVarRatioMinimum_highEnergy")),
       filterByTracksterPID_(ps.getParameter<bool>("filterByTracksterPID")),
       tracksterPIDCategoriesToFilter_(ps.getParameter<std::vector<int>>("tracksterPIDCategoriesToFilter")),
       PIDThreshold_(ps.getParameter<double>("PIDThreshold")) {
   assert(onnxRuntime_ &&
          "TracksterLinkingbySuperClusteringDNN : ONNXRuntime was not provided, the model should have been set in "
          "onnxModelPath in the plugin config");
 }

◆ ~TracksterLinkingbySuperClusteringDNN()

ticl::TracksterLinkingbySuperClusteringDNN::~TracksterLinkingbySuperClusteringDNN ( )

inlineoverride

Definition at line 35 of file TracksterLinkingbySuperClusteringDNN.h.

35 {}

Member Function Documentation

◆ checkExplainedVarianceRatioCut()

bool TracksterLinkingbySuperClusteringDNN::checkExplainedVarianceRatioCut ( ticl::Trackster const & ts ) const

private

Check if trackster passes cut on explained variance ratio. The DNN is trained only on pairs where both seed and candidate pass this cut Explained variance ratio is (largest PCA eigenvalue) / (sum of PCA eigenvalues)

Definition at line 72 of file TracksterLinkingbySuperClusteringDNN.cc.

References ticl::Trackster::eigenvalues(), explVarRatioCut_energyBoundary_, explVarRatioMinimum_highEnergy_, explVarRatioMinimum_lowEnergy_, f, and ticl::Trackster::raw_energy().

Referenced by linkTracksters().

                                                                                                        {
   float explVar_denominator =
       std::accumulate(std::begin(ts.eigenvalues()), std::end(ts.eigenvalues()), 0.f, std::plus<float>());
   if (explVar_denominator != 0.) {
     float explVarRatio = ts.eigenvalues()[0] / explVar_denominator;
     if (ts.raw_energy() > explVarRatioCut_energyBoundary_)
       return explVarRatio >= explVarRatioMinimum_highEnergy_;
     else
       return explVarRatio >= explVarRatioMinimum_lowEnergy_;
   } else
     return false;
 }

◆ fillPSetDescription()

void TracksterLinkingbySuperClusteringDNN::fillPSetDescription ( edm::ParameterSetDescription & iDesc )

static

Definition at line 340 of file TracksterLinkingbySuperClusteringDNN.cc.

References submitPVResolutionJobs::desc, TrackValidation_cff::e5, ticl::Trackster::electron, ticl::TracksterLinkingAlgoBase::fillPSetDescription(), and ticl::Trackster::photon.

                                                                                                {
   TracksterLinkingAlgoBase::fillPSetDescription(desc);  // adds algo_verbosity
   desc.add<edm::FileInPath>("onnxModelPath")->setComment("Path to DNN (as ONNX model)");
   desc.ifValue(edm::ParameterDescription<std::string>("dnnInputsVersion", "v2", true),
                edm::allowedValues<std::string>("v1", "v2"))
       ->setComment(
           "DNN inputs version tag. Defines which set of features is fed to the DNN. Must match with the actual DNN.");
   desc.add<unsigned int>("inferenceBatchSize", 1e5)
       ->setComment(
           "Size of inference batches fed to DNN. Increasing it should produce faster inference but higher memory "
           "usage. "
           "Has no physics impact.");
   desc.add<double>("nnWorkingPoint")
       ->setComment("Working point of DNN (in [0, 1]). DNN score above WP will attempt to supercluster.");
   desc.add<double>("deltaEtaWindow", 0.1)
       ->setComment(
           "Size of delta eta window to consider for superclustering. Seed-candidate pairs outside this window "
           "are not considered for DNN inference.");
   desc.add<double>("deltaPhiWindow", 0.5)
       ->setComment(
           "Size of delta phi window to consider for superclustering. Seed-candidate pairs outside this window "
           "are not considered for DNN inference.");
   desc.add<double>("seedPtThreshold", 4.)
       ->setComment("Minimum transverse energy of trackster to be considered as seed of a supercluster");
   desc.add<double>("candidateEnergyThreshold", 2.)
       ->setComment("Minimum energy of trackster to be considered as candidate for superclustering");
   desc.add<double>("explVarRatioCut_energyBoundary", 50.)
       ->setComment("Boundary energy between low and high energy explVarRatio cut threshold");
   desc.add<double>("explVarRatioMinimum_lowEnergy", 0.92)
       ->setComment(
           "Cut on explained variance ratio of tracksters to be considered as candidate, "
           "for trackster raw_energy < explVarRatioCut_energyBoundary");
   desc.add<double>("explVarRatioMinimum_highEnergy", 0.95)
       ->setComment(
           "Cut on explained variance ratio of tracksters to be considered as candidate, "
           "for trackster raw_energy > explVarRatioCut_energyBoundary");
   desc.add<bool>("filterByTracksterPID", true)->setComment("Filter tracksters before superclustering by PID score");
   desc.add<std::vector<int>>(
           "tracksterPIDCategoriesToFilter",
           {static_cast<int>(Trackster::ParticleType::photon), static_cast<int>(Trackster::ParticleType::electron)})
       ->setComment("List of PID particle types (ticl::Trackster::ParticleType enum) to consider for PID filtering");
   desc.add<double>("PIDThreshold", 0.8)->setComment("PID score threshold");
 }

◆ initialize()

void TracksterLinkingbySuperClusteringDNN::initialize	(	const HGCalDDDConstants *	hgcons,
		const hgcal::RecHitTools	rhtools,
		const edm::ESHandle< MagneticField >	bfieldH,
		const edm::ESHandle< Propagator >	propH
	)

overridevirtual

Implements ticl::TracksterLinkingAlgoBase.

Definition at line 63 of file TracksterLinkingbySuperClusteringDNN.cc.

66 {}

◆ linkTracksters()

void TracksterLinkingbySuperClusteringDNN::linkTracksters	(	const Inputs &	input,
		std::vector< Trackster > &	resultTracksters,
		std::vector< std::vector< unsigned int >> &	outputSuperclusters,
		std::vector< std::vector< unsigned int >> &	linkedTracksterIdToInputTracksterId
	)

overridevirtual

resultTracksters : superclusters as tracksters (ie merging of tracksters that have been superclustered together) outputSuperclusters : same as linkedTracksterIdToInputTracksterId. Probably should use only one of the two. linkedTracksterIdToInputTracksterId : maps indices from resultTracksters back into input tracksters. resultTracksters[i] has seed input.tracksters[linkedTracksterIdToInputTracksterId[i][0]], linked with tracksters input.tracksters[linkedTracksterIdToInputTracksterId[i][1..N]]

Implements ticl::TracksterLinkingAlgoBase.

Definition at line 102 of file TracksterLinkingbySuperClusteringDNN.cc.

References funct::abs(), cms::cuda::assert(), ticl::Trackster::barycenter(), candidateEnergyThreshold_, checkExplainedVarianceRatioCut(), filterCSVwithJSON::copy, deltaEtaWindow_, SiPixelRawToDigiRegional_cfi::deltaPhi, deltaPhiWindow_, dnnInputs_, lowptgsfeleseed::features(), mps_fire::i, testProducerWithPsetDescEmpty_cfi::i1, testProducerWithPsetDescEmpty_cfi::i2, inferenceBatchSize_, input, LogDebug, SiStripPI::max, SiStripPI::min, eostools::move(), nnWorkingPoint_, ticl::TileConstants::nPhiBins, ticl::TracksterLinkingAlgoBase::onnxRuntime_, PatBasicFWLiteJetAnalyzer_Selector_cfg::outputs, ticl::Trackster::raw_energy(), ticl::Trackster::raw_pt(), cms::Ort::ONNXRuntime::run(), alignCSCRings::s, seedPtThreshold_, and trackstersPassesPIDCut().

                                                                              {
   // For now we use all input tracksters for superclustering. At some point there might be a filter here for EM tracksters (electromagnetic identification with DNN ?)
   auto const& inputTracksters = input.tracksters;
   const unsigned int tracksterCount = inputTracksters.size();
 
   /* Sorting tracksters by decreasing order of pT (out-of-place sort). 
   inputTracksters[trackstersIndicesPt[0]], ..., inputTracksters[trackstersIndicesPt[N]] makes a list of tracksters sorted by decreasing pT
   Indices into this pT sorted collection will have the suffix _pt. Thus inputTracksters[index] and inputTracksters[trackstersIndicesPt[index_pt]] are correct
   */
   std::vector<unsigned int> trackstersIndicesPt(inputTracksters.size());
   std::iota(trackstersIndicesPt.begin(), trackstersIndicesPt.end(), 0);
   std::stable_sort(
       trackstersIndicesPt.begin(), trackstersIndicesPt.end(), [&inputTracksters](unsigned int i1, unsigned int i2) {
         return inputTracksters[i1].raw_pt() > inputTracksters[i2].raw_pt();
       });
 
   /* Evaluate in minibatches since running with trackster count = 3000 leads to a short-lived ~15GB memory allocation
   Also we do not know in advance how many superclustering candidate pairs there are going to be
   The batch size needs to be rounded to featureCount
   */
   const unsigned int miniBatchSize =
       static_cast<unsigned int>(inferenceBatchSize_) / dnnInputs_->featureCount() * dnnInputs_->featureCount();
 
   std::vector<std::vector<float>>
       inputTensorBatches;  // DNN input features tensors, in minibatches. Outer array : minibatches, inner array : 2D (flattened) array of features (indexed by batchIndex, featureId)
   // How far along in the latest tensor of inputTensorBatches are we. Set to miniBatchSize to trigger the creation of the tensor batch on first run
   unsigned int candidateIndexInCurrentBatch = miniBatchSize;
   // List of all (ts_seed_id; ts_cand_id) selected for DNN inference (same layout as inputTensorBatches)
   // Index is in global trackster collection (not pt ordered collection)
   std::vector<std::vector<std::pair<unsigned int, unsigned int>>> tracksterIndicesUsedInDNN;
 
   // Use TracksterTiles to speed up search of tracksters in eta-phi window. One per endcap
   std::array<TICLLayerTile, 2> tracksterTilesBothEndcaps_pt;  // one per endcap
   for (unsigned int i_pt = 0; i_pt < trackstersIndicesPt.size(); ++i_pt) {
     Trackster const& ts = inputTracksters[trackstersIndicesPt[i_pt]];
     tracksterTilesBothEndcaps_pt[ts.barycenter().eta() > 0.].fill(ts.barycenter().eta(), ts.barycenter().phi(), i_pt);
   }
 
   // First loop on candidate tracksters (start at 1 since the highest pt trackster can only be a seed, not a candidate)
   for (unsigned int ts_cand_idx_pt = 1; ts_cand_idx_pt < tracksterCount; ts_cand_idx_pt++) {
     Trackster const& ts_cand = inputTracksters[trackstersIndicesPt[ts_cand_idx_pt]];
 
     if (ts_cand.raw_energy() < candidateEnergyThreshold_ ||
         !checkExplainedVarianceRatioCut(ts_cand))  // || !trackstersPassesPIDCut(ts_cand)
       continue;
 
     auto& tracksterTiles = tracksterTilesBothEndcaps_pt[ts_cand.barycenter().eta() > 0];
     std::array<int, 4> search_box = tracksterTiles.searchBoxEtaPhi(ts_cand.barycenter().Eta() - deltaEtaWindow_,
                                                                    ts_cand.barycenter().Eta() + deltaEtaWindow_,
                                                                    ts_cand.barycenter().Phi() - deltaPhiWindow_,
                                                                    ts_cand.barycenter().Phi() + deltaPhiWindow_);
     // Look for seed trackster
     for (int eta_i = search_box[0]; eta_i <= search_box[1]; ++eta_i) {
       for (int phi_i = search_box[2]; phi_i <= search_box[3]; ++phi_i) {
         for (unsigned int ts_seed_idx_pt :
              tracksterTiles[tracksterTiles.globalBin(eta_i, (phi_i % TileConstants::nPhiBins))]) {
           if (ts_seed_idx_pt >= ts_cand_idx_pt)
             continue;  // Look only at seed tracksters with higher pT than the candidate
 
           Trackster const& ts_seed = inputTracksters[trackstersIndicesPt[ts_seed_idx_pt]];
 
           if (ts_seed.raw_pt() < seedPtThreshold_)
             break;  // All further seeds will have lower pT than threshold (due to pT sorting)
 
           if (!checkExplainedVarianceRatioCut(ts_seed) || !trackstersPassesPIDCut(ts_seed))
             continue;
 
           // Check that the two tracksters are geometrically compatible for superclustering
           if (std::abs(ts_seed.barycenter().Eta() - ts_cand.barycenter().Eta()) < deltaEtaWindow_ &&
               std::abs(deltaPhi(ts_seed.barycenter().Phi(), ts_cand.barycenter().Phi())) < deltaPhiWindow_) {
             if (candidateIndexInCurrentBatch >= miniBatchSize) {
               // Create new minibatch
               assert(candidateIndexInCurrentBatch == miniBatchSize);
 
               /* Estimate how many seed-candidate pairs are remaining and don't allocate a full batch in this case. Use worst-case scenario of all pairs passing geometrical window
               Also assume ts_seed_idx_pt=0 (worst case) 
               The last tensor of inputTensorBatches will be larger than necessary. The end of it will be uninitialized, then passed to the DNN.
               We do not look at the output of the DNN on this section, so it will have no consequences.
               */
               inputTensorBatches.emplace_back(
                   std::min(miniBatchSize,
                            (tracksterCount * (tracksterCount - 1) - ts_cand_idx_pt * (ts_cand_idx_pt - 1)) / 2) *
                   dnnInputs_->featureCount());
 
               candidateIndexInCurrentBatch = 0;
               tracksterIndicesUsedInDNN.emplace_back();
             }
 
             std::vector<float> features = dnnInputs_->computeVector(ts_seed, ts_cand);  // Compute DNN features
             assert(features.size() == dnnInputs_->featureCount());
             assert((candidateIndexInCurrentBatch + 1) * dnnInputs_->featureCount() <= inputTensorBatches.back().size());
             // Copy the features into the batch (TODO : could probably avoid the copy and fill straight in the batch vector)
             std::copy(features.begin(),
                       features.end(),
                       inputTensorBatches.back().begin() + candidateIndexInCurrentBatch * dnnInputs_->featureCount());
             candidateIndexInCurrentBatch++;
             tracksterIndicesUsedInDNN.back().emplace_back(trackstersIndicesPt[ts_seed_idx_pt],
                                                           trackstersIndicesPt[ts_cand_idx_pt]);
           }
         }
       }
     }
   }
 
   if (inputTensorBatches.empty()) {
     LogDebug("HGCalTICLSuperclustering")
         << "No superclustering candidate pairs passed preselection before DNN. There are " << tracksterCount
         << " tracksters in this event.";
   }
 
 #ifdef EDM_ML_DEBUG
   if (!inputTensorBatches.empty()) {
     std::ostringstream s;
     // Print the first 20 seed-cndidate pairs sent for inference
     for (unsigned int i = 0;
          i < std::min(dnnInputs_->featureCount() * 20, static_cast<unsigned int>(inputTensorBatches[0].size()));
          i++) {
       s << inputTensorBatches[0][i] << " ";
       if (i != 0 && i % dnnInputs_->featureCount() == 0)
         s << "],\t[";
     }
     LogDebug("HGCalTICLSuperclustering") << inputTensorBatches.size()
                                          << " batches were created. First batch starts as follows : [" << s.str()
                                          << "]";
   }
 #endif
 
   // Run the DNN inference
   std::vector<std::vector<float>>
       batchOutputs;  // Outer index : minibatch, inner index : inference index in minibatch, value : DNN score
   for (std::vector<float>& singleBatch : inputTensorBatches) {
     // ONNXRuntime takes std::vector<std::vector<float>>& as input (non-const reference) so we have to make a new vector
     std::vector<std::vector<float>> inputs_for_onnx{{std::move(singleBatch)}};
     std::vector<float> outputs = onnxRuntime_->run(
         {"input"}, inputs_for_onnx, {}, {}, inputs_for_onnx[0].size() / dnnInputs_->featureCount())[0];
     batchOutputs.push_back(std::move(outputs));
   }
 
   /* Build mask of tracksters already superclustered as candidates (to avoid using a trackster superclustered as candidate as a seed in further iterations).
   Also mask seeds (only needed to add tracksters not in a supercluster to the output). */
   std::vector<bool> tracksterMask(tracksterCount, false);
 
   /* Index of the seed trackster of the previous iteration 
   Initialized with an id that cannot be obtained in input */
   unsigned int previousCandTrackster_idx = std::numeric_limits<unsigned int>::max();
   unsigned int bestSeedForCurrentCandidate_idx = std::numeric_limits<unsigned int>::max();
   float bestSeedForCurrentCandidate_dnnScore = nnWorkingPoint_;
 
   // Lambda to be called when there is a transition from one candidate to the next (as well as after the last iteration)
   // Does the actual supercluster creation
   auto onCandidateTransition = [&](unsigned ts_cand_idx) {
     if (bestSeedForCurrentCandidate_idx <
         std::numeric_limits<unsigned int>::max()) {  // At least one seed can be superclustered with the candidate
       tracksterMask[ts_cand_idx] = true;  // Mask the candidate so it is not considered as seed in later iterations
 
       // Look for a supercluster of the seed
       std::vector<std::vector<unsigned int>>::iterator seed_supercluster_it =
           std::find_if(outputSuperclusters.begin(),
                        outputSuperclusters.end(),
                        [bestSeedForCurrentCandidate_idx](std::vector<unsigned int> const& sc) {
                          return sc[0] == bestSeedForCurrentCandidate_idx;
                        });
 
       if (seed_supercluster_it == outputSuperclusters.end()) {  // No supercluster exists yet for the seed. Create one.
         outputSuperclusters.emplace_back(std::initializer_list<unsigned int>{bestSeedForCurrentCandidate_idx});
         resultTracksters.emplace_back(inputTracksters[bestSeedForCurrentCandidate_idx]);
         linkedTracksterIdToInputTracksterId.emplace_back(
             std::initializer_list<unsigned int>{bestSeedForCurrentCandidate_idx});
         seed_supercluster_it = outputSuperclusters.end() - 1;
         tracksterMask[bestSeedForCurrentCandidate_idx] =
             true;  // mask the seed as well (needed to find tracksters not in any supercluster)
       }
       // Index of the supercluster into resultTracksters, outputSuperclusters and linkedTracksterIdToInputTracksterId collections (the indices are the same)
       unsigned int indexIntoOutputTracksters = seed_supercluster_it - outputSuperclusters.begin();
       seed_supercluster_it->push_back(ts_cand_idx);
       resultTracksters[indexIntoOutputTracksters].mergeTracksters(inputTracksters[ts_cand_idx]);
       linkedTracksterIdToInputTracksterId[indexIntoOutputTracksters].push_back(ts_cand_idx);
 
       assert(outputSuperclusters.size() == resultTracksters.size() &&
              outputSuperclusters.size() == linkedTracksterIdToInputTracksterId.size());
       assert(seed_supercluster_it->size() == linkedTracksterIdToInputTracksterId[indexIntoOutputTracksters].size());
 
       bestSeedForCurrentCandidate_idx = std::numeric_limits<unsigned int>::max();
       bestSeedForCurrentCandidate_dnnScore = nnWorkingPoint_;
     }
   };
 
   //Iterate over minibatches
   for (unsigned int batchIndex = 0; batchIndex < batchOutputs.size(); batchIndex++) {
     std::vector<float> const& currentBatchOutputs = batchOutputs[batchIndex];  // DNN score outputs
     // Iterate over seed-candidate pairs inside current minibatch
     for (unsigned int indexInBatch = 0; indexInBatch < tracksterIndicesUsedInDNN[batchIndex].size(); indexInBatch++) {
       assert(indexInBatch < static_cast<unsigned int>(batchOutputs[batchIndex].size()));
 
       const unsigned int ts_seed_idx = tracksterIndicesUsedInDNN[batchIndex][indexInBatch].first;
       const unsigned int ts_cand_idx = tracksterIndicesUsedInDNN[batchIndex][indexInBatch].second;
       const float currentDnnScore = currentBatchOutputs[indexInBatch];
 
       if (previousCandTrackster_idx != std::numeric_limits<unsigned int>::max() &&
           ts_cand_idx != previousCandTrackster_idx) {
         // There is a transition from one seed to the next (don't make a transition for the first iteration)
         onCandidateTransition(previousCandTrackster_idx);
       }
 
       if (currentDnnScore > bestSeedForCurrentCandidate_dnnScore && !tracksterMask[ts_seed_idx]) {
         // Check that the DNN suggests superclustering, that this seed-candidate assoc is better than previous ones, and that the seed is not already in a supercluster as candidate
         bestSeedForCurrentCandidate_idx = ts_seed_idx;
         bestSeedForCurrentCandidate_dnnScore = currentDnnScore;
       }
       previousCandTrackster_idx = ts_cand_idx;
     }
   }
   onCandidateTransition(previousCandTrackster_idx);
 
   // Adding one-trackster superclusters for all tracksters not in a supercluster already that pass the seed threshold
   for (unsigned int ts_id = 0; ts_id < tracksterCount; ts_id++) {
     if (!tracksterMask[ts_id] && inputTracksters[ts_id].raw_pt() >= seedPtThreshold_) {
       outputSuperclusters.emplace_back(std::initializer_list<unsigned int>{ts_id});
       resultTracksters.emplace_back(inputTracksters[ts_id]);
       linkedTracksterIdToInputTracksterId.emplace_back(std::initializer_list<unsigned int>{ts_id});
     }
   }
 
 #ifdef EDM_ML_DEBUG
   for (std::vector<unsigned int> const& sc : outputSuperclusters) {
     std::ostringstream s;
     for (unsigned int trackster_id : sc)
       s << trackster_id << " ";
     LogDebug("HGCalTICLSuperclustering") << "Created supercluster of size " << sc.size()
                                          << " holding tracksters (first one is seed) " << s.str();
   }
 #endif
 }

◆ trackstersPassesPIDCut()

bool TracksterLinkingbySuperClusteringDNN::trackstersPassesPIDCut ( const Trackster & ts ) const

private

Definition at line 85 of file TracksterLinkingbySuperClusteringDNN.cc.

References eostools::cat(), filterByTracksterPID_, ticl::Trackster::id_probabilities(), PIDThreshold_, and tracksterPIDCategoriesToFilter_.

Referenced by linkTracksters().

                                                                                             {
   if (filterByTracksterPID_) {
     float probTotal = 0.0f;
     for (int cat : tracksterPIDCategoriesToFilter_) {
       probTotal += tst.id_probabilities(cat);
     }
     return probTotal >= PIDThreshold_;
   } else
     return true;
 }