#include <SegmentEdgeInferenceAlg.h>

Inheritance diagram for MuonML::SegmentEdgeInferenceAlg:

Collaboration diagram for MuonML::SegmentEdgeInferenceAlg:

Classes
struct	TruthLossCounters
	Job-summed classification of lost truth segments (classifyLostTruthSegments()). More...
struct	GroupPurityCounters
	Job-summed grouping-purity diagnostics: does a formed (kept) component mix segments from more than one truth muon, and does each truth muon's retained segments end up in one clean component, a component shared with another muon, or split across several components? More...

Public Member Functions
StatusCode	initialize () override
StatusCode	execute (const EventContext &ctx) const override
StatusCode	finalize () override
	Log of the post-ONNX rank/mutuality and min-component-size selection.
virtual StatusCode	sysInitialize () override
	Override sysInitialize.
virtual bool	isClonable () const override
	Specify if the algorithm is clonable.
virtual StatusCode	sysExecute (const EventContext &ctx) override
	Execute an algorithm.
virtual const DataObjIDColl &	extraOutputDeps () const override
	Return the list of extra output dependencies.
virtual bool	filterPassed (const EventContext &ctx) const
	Get filter decision:
virtual void	setFilterPassed (bool state, const EventContext &ctx) const
	Set filter decision:
ServiceHandle< StoreGateSvc > &	evtStore ()
	The standard `StoreGateSvc` (event store) Returns (kind of) a pointer to the `StoreGateSvc`.
const ServiceHandle< StoreGateSvc > &	detStore () const
	The standard `StoreGateSvc/DetectorStore` Returns (kind of) a pointer to the `StoreGateSvc`.
virtual StatusCode	sysStart () override
	Handle START transition.
virtual std::vector< Gaudi::DataHandle * >	inputHandles () const override
	Return this algorithm's input handles.
virtual std::vector< Gaudi::DataHandle * >	outputHandles () const override
	Return this algorithm's output handles.
Gaudi::Details::PropertyBase &	declareProperty (Gaudi::Property< T, V, H > &t)
void	updateVHKA (Gaudi::Details::PropertyBase &)
MsgStream &	msg () const
bool	msgLvl (const MSG::Level lvl) const

Protected Member Functions
void	renounceArray (SG::VarHandleKeyArray &handlesArray)
	remove all handles from I/O resolution
std::enable_if_t< std::is_void_v< std::result_of_t< decltype(&T::renounce)(T)> > &&!std::is_base_of_v< SG::VarHandleKeyArray, T > &&std::is_base_of_v< Gaudi::DataHandle, T >, void >	renounce (T &h)
void	extraDeps_update_handler (Gaudi::Details::PropertyBase &ExtraDeps)
	Add StoreName to extra input/output deps as needed.

Private Types
typedef ServiceHandle< StoreGateSvc >	StoreGateSvc_t

Private Member Functions
void	classifyLostTruthSegments (const xAOD::MuonSegmentContainer &segments, const SegmentEdgeGraph &graph, const std::unordered_map< std::uint64_t, float > &pairProbability, const std::vector< unsigned char > &thresholded, const std::vector< std::uint8_t > &keptNode, const std::vector< unsigned char > &nodeDropReason) const
	MC-only: classify every truth-labeled input segment that did not make it into the filtered container by the stage that removed it.
void	classifyGroupPurity (const std::unordered_map< std::int32_t, std::unordered_set< unsigned > > &muonComponents, const std::unordered_map< std::int32_t, bool > &muonEverMixed, const std::unordered_map< std::int32_t, double > &truthIdToAbsEta) const
	MC-only: given the per-muon component memberships and mixed-status gathered while the per-component classify every truth muon's final.
Gaudi::Details::PropertyBase &	declareGaudiProperty (Gaudi::Property< T, V, H > &hndl, const SG::VarHandleKeyType &)
	specialization for handling Gaudi::Property<SG::VarHandleKey>

Private Attributes
SG::ReadHandleKey< xAOD::MuonSegmentContainer >	m_segmentKey {this, "SegmentKey", "MsSegments"}
SG::WriteDecorHandleKey< xAOD::MuonSegmentContainer >	m_pairGateDecorKey
	Per-segment payload consumed by MlMsTrackSeeder: [componentId, anchorRank + 1] Empty means that the segment was rejected by the ML preselection.
SG::WriteDecorHandleKey< xAOD::MuonSegmentContainer >	m_componentScoreDecorKey
	Per-segment ML scores for MlMsTrackSeeder: [strongest selected cross-layer edge of the segment's component, strongest selected cross-layer edge of the segment itself] (-1 if none).
SG::WriteHandleKey< ConstDataVector< xAOD::MuonSegmentContainer > >	m_filteredSegmentKey
Gaudi::Property< float >	m_pairGateThreshold
Gaudi::Property< unsigned int >	m_maxEdgesPerNode
Gaudi::Property< unsigned int >	m_maxEdgesPerNodePerLayer
Gaudi::Property< bool >	m_useDegreeCappedComponents
Gaudi::Property< bool >	m_requireMutualTopKEdges
Gaudi::Property< bool >	m_recoverOrphanNodes
Gaudi::Property< unsigned int >	m_seedAnchorsPerComponent
Gaudi::Property< bool >	m_anchorInnermostLayer
Gaudi::Property< bool >	m_keepBestSegmentPerChamber
Gaudi::Property< unsigned int >	m_minSegmentsPerComponent
ToolHandle< ISegmentEdgeClassifierTool >	m_edgeClassifier {this, "EdgeClassifierTool", "MuonML::SegmentEdgeClassifierTool/SegmentEdgeClassifierTool"}
bool	m_truthDiagEnabled {false}
	Whether truth-particle matching is available/enabled this job.
std::atomic< std::size_t >	m_sumGraphNodes {0}
	Job-summed post-ONNX selection counters (see execute()).
std::atomic< std::size_t >	m_sumThresholdedNodes {0}
std::atomic< std::size_t >	m_sumMutualTopKPairs {0}
std::atomic< std::size_t >	m_sumOneSidedTopKPairs {0}
std::atomic< std::size_t >	m_sumOrphanRecoveryPairs {0}
std::atomic< std::size_t >	m_sumTopologyNodes {0}
std::atomic< std::size_t >	m_sumRetainedNodes {0}
std::atomic< std::size_t >	m_sumChamberSuppressedNodes {0}
std::atomic< std::size_t >	m_sumNodesRejectedByMinComponent {0}
std::atomic< std::size_t >	m_sumRejectedComponents {0}
std::atomic< std::size_t >	m_sumComponentsKept {0}
std::atomic< std::size_t >	m_sumInputTruthSegments {0}
std::atomic< std::size_t >	m_sumInputBkgSegments {0}
std::atomic< std::size_t >	m_sumOnnxTruthSegments {0}
std::atomic< std::size_t >	m_sumOnnxBkgSegments {0}
std::atomic< std::size_t >	m_sumRetainedTruthSegments {0}
std::atomic< std::size_t >	m_sumRetainedBkgSegments {0}
std::atomic< std::size_t >	m_sumTrueEdgesTotal {0}
std::atomic< std::size_t >	m_sumTrueEdgesPassed {0}
std::array< std::atomic< std::size_t >, 2 > m_sumTrueCrossLayerEdges	ATLAS_THREAD_SAFE {}
	True cross-layer edges above PairGateThreshold, total and kept by the selection; barrel / endcap Every element is a std::atomic, updated only with atomic increments.
std::array< std::atomic< std::size_t >, 2 > m_sumTrueCrossLayerSelected	ATLAS_THREAD_SAFE {}
std::atomic< std::size_t >	m_sumBkgEdgesTotal {0}
std::atomic< std::size_t >	m_sumBkgEdgesPassed {0}
TruthLossCounters m_truthLoss	ATLAS_THREAD_SAFE
	Every element is a std::atomic, updated only with atomic additions.
std::atomic< bool >	m_warnedMissingToolDiagnostics {false}
GroupPurityCounters m_groupPurity	ATLAS_THREAD_SAFE
	Every element is a std::atomic, updated only with atomic additions/CAS.
DataObjIDColl	m_extendedExtraObjects
	Extra output dependency collection, extended by AthAlgorithmDHUpdate to add symlinks.
StoreGateSvc_t	m_evtStore
	Pointer to StoreGate (event store by default).
StoreGateSvc_t	m_detStore
	Pointer to StoreGate (detector store by default).
std::vector< SG::VarHandleKeyArray * >	m_vhka
bool	m_varHandleArraysDeclared

Detailed Description

Definition at line 25 of file SegmentEdgeInferenceAlg.h.

Member Typedef Documentation

◆ StoreGateSvc_t

typedef ServiceHandle<StoreGateSvc> AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::StoreGateSvc_t

privateinherited

Definition at line 376 of file AthCommonDataStore.h.

Member Function Documentation

◆ classifyGroupPurity()

void MuonML::SegmentEdgeInferenceAlg::classifyGroupPurity	(	const std::unordered_map< std::int32_t, std::unordered_set< unsigned > > &	muonComponents,
		const std::unordered_map< std::int32_t, bool > &	muonEverMixed,
		const std::unordered_map< std::int32_t, double > &	truthIdToAbsEta ) const

private

MC-only: given the per-muon component memberships and mixed-status gathered while the per-component classify every truth muon's final.

Definition at line 912 of file SegmentEdgeInferenceAlg.cxx.

                                                                       {
  // Only muons with at least one retained segment in some kept component
  // appear in muonComponents; fully-lost muons are already covered by
  // classifyLostTruthSegments()/muonsSeedLost above, not double-counted here.
  std::array<std::size_t, GroupPurityCounters::kColumns * 3> outcome{};
  for (const auto& [id, componentIds] : muonComponents) {
    const auto mixedIt = muonEverMixed.find(id);
    const bool everMixed = mixedIt != muonEverMixed.end() && mixedIt->second;
    // 0 = clean (one component, alone), 1 = mixed (shared a component with
    // another muon), 2 = fragmented (split across components, never mixed).
    const std::size_t result = everMixed ? 1 : (componentIds.size() >= 2 ? 2 : 0);
    const auto etaIt = truthIdToAbsEta.find(id);
    const std::size_t region = 1 + (etaIt != truthIdToAbsEta.end()
        ? static_cast<std::size_t>(regionOf(etaIt->second)) : 0);
    ++outcome[0 * 3 + result];
    ++outcome[region * 3 + result];
  }
  for (std::size_t i = 0; i < outcome.size(); ++i) m_groupPurity.muonOutcome[i] += outcome[i];
}

◆ classifyLostTruthSegments()

void MuonML::SegmentEdgeInferenceAlg::classifyLostTruthSegments	(	const xAOD::MuonSegmentContainer &	segments,
		const SegmentEdgeGraph &	graph,
		const std::unordered_map< std::uint64_t, float > &	pairProbability,
		const std::vector< unsigned char > &	thresholded,
		const std::vector< std::uint8_t > &	keptNode,
		const std::vector< unsigned char > &	nodeDropReason ) const

private

MC-only: classify every truth-labeled input segment that did not make it into the filtered container by the stage that removed it.

Called only when truth diagnostics are on.

Definition at line 733 of file SegmentEdgeInferenceAlg.cxx.

                                                          {
  static_assert(kNumLossCategories == TruthLossCounters::kCategories);
  constexpr std::size_t kColumns = TruthLossCounters::kColumns;
  const std::size_t nInput = segments.size();
 
  if (graph.inputNodeIndex.size() != nInput) {
    if (!m_warnedMissingToolDiagnostics.exchange(true)) {
      ATH_MSG_WARNING("Truth diagnostics requested, but the edge classifier tool "
                      "did not provide its pre-ONNX fates; set "
                      "EnableTruthDiagnostics on the tool as well. Skipping the "
                      "lost-segment classification.");
    }
    return;
  }
 
  // Truth particle of every input segment and the segments grouped per particle.
  std::vector<const xAOD::TruthParticle*> truthPart(nInput, nullptr);
  std::vector<std::int32_t> truthId(nInput, -1);
  std::unordered_map<std::int32_t, std::vector<std::uint32_t>> byTruth;
  {
    std::uint32_t inputIndex = 0;
    for (const xAOD::MuonSegment* segment : segments) {
      if (const xAOD::TruthParticle* part = MuonR4::getTruthMatchedParticle(*segment)) {
        truthPart[inputIndex] = part;
        truthId[inputIndex] = static_cast<std::int32_t>(part->index());
        byTruth[truthId[inputIndex]].push_back(inputIndex);
      }
      ++inputIndex;
    }
  }
  const auto isRetained = [&](std::size_t input) {
    const std::int32_t node = graph.inputNodeIndex[input];
    return node >= 0 && keptNode[static_cast<std::size_t>(node)];
  };
 
  // Per segment: how far its best cross-chamber true pair got (1 = bucket cap,
  // 2 = sector window, 3 = angle window, 4 = edge caps, 5 = scored, 0 = only
  // same-chamber partners) and the score / index of its best scored pair.
  const auto rankOf = [](PairGate gate) -> unsigned char {
    switch (gate) {
      case PairGate::BucketCap: return 1;
      case PairGate::SectorWindow: return 2;
      case PairGate::AngleWindow: return 3;
      case PairGate::EdgeCaps: return 4;
      case PairGate::Scored: return 5;
      case PairGate::SameChamber: break;
    }
    return 0;
  };
  std::vector<unsigned char> bestRank(nInput, 0);
  std::vector<float> bestScore(nInput, -1.f);
  std::vector<std::int32_t> bestScoredPair(nInput, -1);
  for (std::size_t k = 0; k < graph.truthPairFates.size(); ++k) {
    const PairFate& fate = graph.truthPairFates[k];
    const unsigned char rank = rankOf(fate.gate);
    if (rank == 0) continue;
    const std::array<std::uint32_t, 2> ends{fate.first, fate.second};
    for (const std::uint32_t end : ends) bestRank[end] = std::max(bestRank[end], rank);
    if (fate.gate != PairGate::Scored) continue;
    const std::int32_t firstNode = graph.inputNodeIndex[fate.first];
    const std::int32_t secondNode = graph.inputNodeIndex[fate.second];
    if (firstNode < 0 || secondNode < 0) continue;
    const auto found = pairProbability.find(undirectedPairKey(
        static_cast<std::size_t>(firstNode), static_cast<std::size_t>(secondNode)));
    const float score = found != pairProbability.end() ? found->second : 0.f;
    for (const std::uint32_t end : ends) {
      if (score > bestScore[end]) {
        bestScore[end] = score;
        bestScoredPair[end] = static_cast<std::int32_t>(k);
      }
    }
  }
 
  // Seedability per truth muon: at least 2 distinct chambers in the input,
  // fewer than 2 among the retained segments.
  std::unordered_map<std::int32_t, bool> seedLostByTruth;
  std::array<std::size_t, 4> muonsSeedable{};
  std::array<std::size_t, 4> muonsSeedLost{};
  for (const auto& entry : byTruth) {
    std::unordered_set<int> inputChambers;
    std::unordered_set<int> retainedChambers;
    for (const std::uint32_t input : entry.second) {
      const int chamber = static_cast<int>(segments[input]->chamberIndex());
      inputChambers.insert(chamber);
      if (isRetained(input)) retainedChambers.insert(chamber);
    }
    const bool seedable = inputChambers.size() >= 2;
    const bool lost = seedable && retainedChambers.size() < 2;
    seedLostByTruth[entry.first] = lost;
    if (!seedable) continue;
    const std::size_t region = 1 + static_cast<std::size_t>(
        regionOf(std::abs(truthPart[entry.second.front()]->eta())));
    ++muonsSeedable[0];
    ++muonsSeedable[region];
    if (lost) {
      ++muonsSeedLost[0];
      ++muonsSeedLost[region];
    }
  }
 
  std::array<std::size_t, kNumLossCategories * kColumns> category{};
  std::array<std::size_t, 5> scoreBin{};
  std::array<std::size_t, 2> sectorDelta{};
  std::array<std::size_t, 4> layerPair{};
  std::array<std::size_t, 4> muonSegments{};
  std::array<std::size_t, 3> precisionHits{};
  for (std::size_t i = 0; i < nInput; ++i) {
    if (truthId[i] < 0 || isRetained(i)) continue;
    const std::vector<std::uint32_t>& members = byTruth[truthId[i]];
    const std::int32_t node = graph.inputNodeIndex[i];
 
    std::size_t cat{};
    if (members.size() == 1) {
      cat = kNoPartnerSingleton;
    } else if (bestRank[i] == 0) {
      cat = kNoPartnerSameChamber;
    } else if (node == kDroppedByBucketCap) {
      cat = kBucketSelf;
    } else if (node >= 0 && thresholded[static_cast<std::size_t>(node)]) {
      switch (nodeDropReason[static_cast<std::size_t>(node)]) {
        case kReasonChamberDedup: cat = kDedupChamber; break;
        case kReasonComponentRejected: cat = kComponentRejected; break;
        default: cat = kNotSelected; break;
      }
    } else {
      switch (bestRank[i]) {
        case 5: cat = kBelowThreshold; break;
        case 4: cat = kEdgeCaps; break;
        case 3: cat = kAngleWindow; break;
        case 2: cat = kSectorWindow; break;
        default: cat = kBucketPartners; break;
      }
    }
 
    const std::size_t region = 1 + static_cast<std::size_t>(
        regionOf(std::abs(truthPart[i]->eta())));
    ++category[cat * kColumns];
    ++category[cat * kColumns + region];
    if (seedLostByTruth[truthId[i]]) ++category[cat * kColumns + 4];
 
    if (cat != kBelowThreshold) continue;
    if (bestScoredPair[i] >= 0) {
      const PairFate& fate = graph.truthPairFates[static_cast<std::size_t>(bestScoredPair[i])];
      const float score = bestScore[i];
      ++scoreBin[score < 1e-3f ? 0 : score < 1e-2f ? 1 : score < 2e-2f ? 2
                                                         : score < 5e-2f ? 3 : 4];
      ++sectorDelta[fate.sectorDelta == 0 ? 0 : 1];
      const std::uint32_t other = fate.first == i ? fate.second : fate.first;
      int rankA = layerRadialRank(Muon::MuonStationIndex::toLayerIndex(
          segments[i]->chamberIndex()));
      int rankB = layerRadialRank(Muon::MuonStationIndex::toLayerIndex(
          segments[other]->chamberIndex()));
      if (rankA > rankB) std::swap(rankA, rankB);
      ++layerPair[(rankA == 0 && rankB == 1) ? 0
                  : (rankA == 1 && rankB == 2) ? 1
                  : (rankA == 0 && rankB == 2) ? 2 : 3];
    }
    ++muonSegments[std::min<std::size_t>(members.size(), 5) - 2];
    const unsigned int hits = segments[i]->nPrecisionHits();
    ++precisionHits[hits <= 4 ? 0 : (hits <= 6 ? 1 : 2)];
  }
 
  for (std::size_t c = 0; c < category.size(); ++c) m_truthLoss.category[c] += category[c];
  for (std::size_t c = 0; c < scoreBin.size(); ++c) m_truthLoss.scoreBin[c] += scoreBin[c];
  for (std::size_t c = 0; c < sectorDelta.size(); ++c) m_truthLoss.sectorDelta[c] += sectorDelta[c];
  for (std::size_t c = 0; c < layerPair.size(); ++c) m_truthLoss.layerPair[c] += layerPair[c];
  for (std::size_t c = 0; c < muonSegments.size(); ++c) m_truthLoss.muonSegments[c] += muonSegments[c];
  for (std::size_t c = 0; c < precisionHits.size(); ++c) m_truthLoss.precisionHits[c] += precisionHits[c];
  for (std::size_t c = 0; c < muonsSeedable.size(); ++c) {
    m_truthLoss.muonsSeedable[c] += muonsSeedable[c];
    m_truthLoss.muonsSeedLost[c] += muonsSeedLost[c];
  }
}

◆ declareGaudiProperty()

Gaudi::Details::PropertyBase & AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::declareGaudiProperty	(	Gaudi::Property< T, V, H > &	hndl,
		const SG::VarHandleKeyType &	)

inlineprivateinherited

specialization for handling Gaudi::Property<SG::VarHandleKey>

Definition at line 156 of file AthCommonDataStore.h.

  {
    return *AthCommonDataStore<PBASE>::declareProperty(hndl.name(),
                                                       hndl.value(), 
                                                       hndl.documentation());
 
  }

◆ declareProperty()

Gaudi::Details::PropertyBase & AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::declareProperty ( Gaudi::Property< T, V, H > & t )

inlineinherited

Definition at line 145 of file AthCommonDataStore.h.

                                                                     {
    typedef typename SG::HandleClassifier<T>::type htype;
    return AthCommonDataStore<PBASE>::declareGaudiProperty(t, htype());
  }

◆ detStore()

const ServiceHandle< StoreGateSvc > & AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::detStore ( ) const

inlineinherited

The standard StoreGateSvc/DetectorStore Returns (kind of) a pointer to the StoreGateSvc.

Definition at line 95 of file AthCommonDataStore.h.

95{ return m_detStore; }

AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::m_detStore

StoreGateSvc_t m_detStore

Definition AthCommonDataStore.h:381

◆ evtStore()

ServiceHandle< StoreGateSvc > & AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::evtStore ( )

inlineinherited

The standard StoreGateSvc (event store) Returns (kind of) a pointer to the StoreGateSvc.

Definition at line 85 of file AthCommonDataStore.h.

85{ return m_evtStore; }

AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::m_evtStore

StoreGateSvc_t m_evtStore

Definition AthCommonDataStore.h:378

◆ execute()

StatusCode MuonML::SegmentEdgeInferenceAlg::execute ( const EventContext & ctx ) const

override

Definition at line 128 of file SegmentEdgeInferenceAlg.cxx.

                                                                         {
  const xAOD::MuonSegmentContainer* segments{};
  ATH_CHECK(SG::get(segments, m_segmentKey, ctx));
  // Evaluated once per event; short-circuits without touching the message
  // service unless the property was explicitly enabled.
  const bool truthDiag = m_truthDiagEnabled && msgLvl(MSG::DEBUG);
  ATH_MSG_DEBUG("Event " << ctx.eventID().event_number()
                << ": input segments in '" << m_segmentKey.key()
                << "' = " << segments->size());
 
  if (truthDiag) {
    std::size_t truthSegs = 0, bkgSegs = 0;
    for (const xAOD::MuonSegment* seg : *segments) {
      MuonR4::getTruthMatchedParticle(*seg) ? ++truthSegs : ++bkgSegs;
    }
    m_sumInputTruthSegments += truthSegs;
    m_sumInputBkgSegments += bkgSegs;
  }
 
  SegmentEdgeGraph graph{};
  std::vector<SegmentEdgeScore> scores{};
  ATH_CHECK(m_edgeClassifier->buildGraph(ctx, *segments, graph));
  ATH_MSG_DEBUG("Event " << ctx.eventID().event_number()
                << ": built graph with nodes=" << graph.nNodes
                << ", edges=" << graph.nEdges);
 
  // Per-node truth-particle grouping key (-1 = unlabeled), used for the
  // segment-level funnel and for the edge-level true/background split.
  std::vector<int32_t> nodeTruthId;
  // Truth-particle id -> |eta|, filled alongside nodeTruthId.
  std::unordered_map<int32_t, double> truthIdToAbsEta;
  if (truthDiag) {
    nodeTruthId.assign(graph.nNodes, -1);
    std::size_t truthSegs = 0, bkgSegs = 0;
    for (std::size_t node = 0; node < graph.nNodes; ++node) {
      const xAOD::TruthParticle* truthPart =
          MuonR4::getTruthMatchedParticle(*graph.segments[node]);
      if (truthPart) {
        nodeTruthId[node] = static_cast<int32_t>(truthPart->index());
        truthIdToAbsEta.emplace(nodeTruthId[node], std::abs(truthPart->eta()));
        ++truthSegs;
      } else {
        ++bkgSegs;
      }
    }
    m_sumOnnxTruthSegments += truthSegs;
    m_sumOnnxBkgSegments += bkgSegs;
  }
 
  ATH_CHECK(m_edgeClassifier->classifyEdges(ctx, graph, scores));
  if (!scores.empty()) {
    float minProb = std::numeric_limits<float>::max();
    float maxProb = std::numeric_limits<float>::lowest();
    for (const SegmentEdgeScore& score : scores) {
      minProb = std::min(minProb, score.probability);
      maxProb = std::max(maxProb, score.probability);
    }
    ATH_MSG_DEBUG("Event " << ctx.eventID().event_number()
                  << ": edge scores=" << scores.size()
                  << ", prob range=[" << minProb << ", " << maxProb << "]");
  } else {
    ATH_MSG_DEBUG("Event " << ctx.eventID().event_number()
                  << ": no edge scores produced");
  }
 
  // Symmetrise directed model outputs once.  Everything downstream uses the
  // score of an undirected segment association.
  std::unordered_map<std::uint64_t, float> pairProbability;
  pairProbability.reserve(scores.size());
  for (const SegmentEdgeScore& score : scores) {
    if (score.src >= graph.nNodes || score.dst >= graph.nNodes ||
        score.src == score.dst) {
      continue;
    }
    const std::uint64_t key = undirectedPairKey(score.src, score.dst);
    auto [it, inserted] = pairProbability.emplace(key, score.probability);
    if (!inserted) it->second = std::max(it->second, score.probability);
  }
 
  // Edge-level recall/false-positive check: candidate pairs that reached ONNX 
  // true pairs reach PairGateThreshold, versus how many background pairs do. 
  if (truthDiag) {
    std::size_t trueTotal = 0, truePassed = 0, bkgTotal = 0, bkgPassed = 0;
    for (const auto& [key, probability] : pairProbability) {
      const auto [first, second] = unpackPairKey(key);
      if (first >= graph.nNodes || second >= graph.nNodes) continue;
      const bool isTrueEdge =
          nodeTruthId[first] >= 0 && nodeTruthId[first] == nodeTruthId[second];
      const bool passed = probability >= m_pairGateThreshold.value();
      if (isTrueEdge) {
        ++trueTotal;
        truePassed += passed;
      } else {
        ++bkgTotal;
        bkgPassed += passed;
      }
    }
    m_sumTrueEdgesTotal += trueTotal;
    m_sumTrueEdgesPassed += truePassed;
    m_sumBkgEdgesTotal += bkgTotal;
    m_sumBkgEdgesPassed += bkgPassed;
  }
 
  // Form a sparse, score-ranked topology. Mutual top-K associations retain
  // locally consistent paths while suppressing one-sided bridges.
  using WeightedEdge = std::pair<std::uint64_t, float>;
  const auto betterWeightedEdge = [](const WeightedEdge& first,
                                      const WeightedEdge& second) {
    const int probabilityOrder = InferenceUtils::compareFloatDescending(
        first.second, second.second);
    if (probabilityOrder != 0) {
      return probabilityOrder < 0;
    }
    return first.first < second.first;
  };
  std::vector<std::vector<WeightedEdge>> edgesByNode(graph.nNodes);
  std::size_t thresholdPairs = 0;
  for (const auto& [key, probability] : pairProbability) {
    if (probability < m_pairGateThreshold.value()) continue;
    const auto [first, second] = unpackPairKey(key);
    if (first >= graph.nNodes || second >= graph.nNodes) continue;
    edgesByNode[first].emplace_back(key, probability);
    edgesByNode[second].emplace_back(key, probability);
    ++thresholdPairs;
  }
 
  std::size_t thresholdedNodes = 0;
  for (const std::vector<WeightedEdge>& nodeEdges : edgesByNode) {
    thresholdedNodes += !nodeEdges.empty();
  }
 
  std::vector<Muon::MuonStationIndex::LayerIndex> nodeLayer(graph.nNodes);
  for (std::size_t node = 0; node < graph.nNodes; ++node) {
    nodeLayer[node] = Muon::MuonStationIndex::toLayerIndex(graph.segments[node]->chamberIndex());
  }
  const unsigned int maxEdgesPerLayer = m_maxEdgesPerNodePerLayer.value();
  std::unordered_map<std::uint64_t, unsigned char> nominations;
  nominations.reserve(thresholdPairs);
  for (std::size_t node = 0; node < graph.nNodes; ++node) {
    std::vector<WeightedEdge>& nodeEdges = edgesByNode[node];
    std::sort(nodeEdges.begin(), nodeEdges.end(), betterWeightedEdge);
    if (maxEdgesPerLayer != 0) {
      // Best edges towards each layer: a segment keeps its partner in every neighbouring
      // layer, and same-layer edges do not compete with cross-layer ones.
      std::array<unsigned int, Acts::toUnderlying(Muon::MuonStationIndex::LayerIndex::LayerIndexMax) + 1> taken{};
      std::erase_if(nodeEdges, [&](const WeightedEdge& edge) {
        const auto [first, second] = unpackPairKey(edge.first);
        const std::size_t other = first == node ? second : first;
        const auto layer = static_cast<std::size_t>(Acts::toUnderlying(nodeLayer[other]) + 1);
        return taken[layer]++ >= maxEdgesPerLayer;
      });
    } else if (m_maxEdgesPerNode.value() != 0 &&
               nodeEdges.size() > m_maxEdgesPerNode.value()) {
      nodeEdges.resize(m_maxEdgesPerNode.value());
    }
    for (const WeightedEdge& edge : nodeEdges) {
      ++nominations[edge.first];
    }
  }
 
  std::size_t mutualTopKPairs = 0;
  std::size_t oneSidedTopKPairs = 0;
  for (const auto& [_, count] : nominations) {
    if (count == 2) {
      ++mutualTopKPairs;
    } else {
      ++oneSidedTopKPairs;
    }
  }
 
  std::unordered_set<std::uint64_t> selectedPairKeys;
  selectedPairKeys.reserve(thresholdPairs);
  if (m_useDegreeCappedComponents.value()) {
    // Apply a score-ordered global degree cap when explicitly requested.
    std::vector<WeightedEdge> acceptedPairs;
    acceptedPairs.reserve(thresholdPairs);
    for (const auto& [key, probability] : pairProbability) {
      if (probability < m_pairGateThreshold.value()) continue;
      acceptedPairs.emplace_back(key, probability);
    }
    std::sort(acceptedPairs.begin(), acceptedPairs.end(), betterWeightedEdge);
 
    const unsigned int maxDegree = m_maxEdgesPerNode.value();
    std::vector<unsigned int> degree(graph.nNodes, 0);
    for (const WeightedEdge& edge : acceptedPairs) {
      const auto [first, second] = unpackPairKey(edge.first);
      if (maxDegree != 0 &&
          (degree[first] >= maxDegree || degree[second] >= maxDegree)) {
        continue;
      }
      selectedPairKeys.insert(edge.first);
      ++degree[first];
      ++degree[second];
    }
  } else {
    for (const auto& [key, count] : nominations) {
      if (m_requireMutualTopKEdges.value() && count != 2) continue;
      selectedPairKeys.insert(key);
    }
  }
 
  // A mutual top-K selection can leave an endpoint without an association.
  // Add its best thresholded edge, at most once per orphaned endpoint.
  std::size_t orphanRecoveryPairs = 0;
  if (!m_useDegreeCappedComponents.value() &&
      m_requireMutualTopKEdges.value() &&
      m_recoverOrphanNodes.value()) {
    std::vector<unsigned char> selectedNode(graph.nNodes, 0);
    for (const std::uint64_t key : selectedPairKeys) {
      const auto [first, second] = unpackPairKey(key);
      if (first < graph.nNodes) selectedNode[first] = 1;
      if (second < graph.nNodes) selectedNode[second] = 1;
    }
    for (std::size_t node = 0; node < graph.nNodes; ++node) {
      if (selectedNode[node] || edgesByNode[node].empty()) continue;
      const std::uint64_t key = edgesByNode[node].front().first;
      const auto [first, second] = unpackPairKey(key);
      if (first >= graph.nNodes || second >= graph.nNodes) continue;
      if (selectedPairKeys.insert(key).second) ++orphanRecoveryPairs;
      selectedNode[first] = 1;
      selectedNode[second] = 1;
    }
  }
 
  std::vector<std::uint64_t> selectedPairs{selectedPairKeys.begin(),
                                           selectedPairKeys.end()};
  std::sort(selectedPairs.begin(), selectedPairs.end());
 
  // Truth diagnostics only: true cross-layer edges above threshold that the selection keeps,
  // barrel / endcap (by the first endpoint's chamber)
  if (truthDiag) {
    for (const auto& [key, probability] : pairProbability) {
      if (probability < m_pairGateThreshold.value()) continue;
      const auto [first, second] = unpackPairKey(key);
      if (first >= graph.nNodes || second >= graph.nNodes) continue;
      if (nodeTruthId[first] < 0 || nodeTruthId[first] != nodeTruthId[second]) continue;
      if (nodeLayer[first] == nodeLayer[second]) continue;
      const std::size_t region =
          Muon::MuonStationIndex::isBarrel(graph.segments[first]->chamberIndex()) ? 0 : 1;
      ++m_sumTrueCrossLayerEdges[region];
      if (selectedPairKeys.contains(key)) ++m_sumTrueCrossLayerSelected[region];
    }
  }
 
  // Truth diagnostics only: best selected-edge score per node, so that the
  // strongest edge holding a component together is a max over its nodes.
  std::vector<float> nodeBestSelectedScore;
  if (truthDiag) {
    nodeBestSelectedScore.assign(graph.nNodes, -1.f);
    for (const std::uint64_t key : selectedPairs) {
      const auto found = pairProbability.find(key);
      if (found == pairProbability.end()) continue;
      const auto [first, second] = unpackPairKey(key);
      nodeBestSelectedScore[first] = std::max(nodeBestSelectedScore[first], found->second);
      nodeBestSelectedScore[second] = std::max(nodeBestSelectedScore[second], found->second);
    }
  }
 
  // Innermost-layer anchoring with an anchor cap: rank the inner candidates by
  // their strongest selected edge towards another layer, i.e. by how firmly
  // the model links them outwards. Same-chamber alternatives otherwise all
  // launch seeds built from the same outer segments.
  const auto layerOf = [&graph](std::size_t node) {
    return Muon::MuonStationIndex::toLayerIndex(graph.segments[node]->chamberIndex());
  };
  std::vector<float> nodeBestCrossLayerScore;
  std::vector<unsigned int> nodeCrossLayerEdges;  // truth diagnostics only
  {
    nodeBestCrossLayerScore.assign(graph.nNodes, -1.f);
    if (truthDiag) nodeCrossLayerEdges.assign(graph.nNodes, 0);
    for (const std::uint64_t key : selectedPairs) {
      const auto [first, second] = unpackPairKey(key);
      if (layerOf(first) == layerOf(second)) continue;
      const auto found = pairProbability.find(key);
      if (found == pairProbability.end()) continue;
      nodeBestCrossLayerScore[first] = std::max(nodeBestCrossLayerScore[first], found->second);
      nodeBestCrossLayerScore[second] = std::max(nodeBestCrossLayerScore[second], found->second);
      if (truthDiag) {
        ++nodeCrossLayerEdges[first];
        ++nodeCrossLayerEdges[second];
      }
    }
  }
 
 
  DisjointSet components{graph.nNodes};
  std::vector<bool> activeNode(graph.nNodes, false);
  for (const std::uint64_t key : selectedPairs) {
    const auto [first, second] = unpackPairKey(key);
    components.unite(first, second);
    activeNode[first] = true;
    activeNode[second] = true;
  }
 
  std::unordered_map<std::size_t, std::vector<std::size_t>> byRoot;
  byRoot.reserve(graph.nNodes);
  for (std::size_t node = 0; node < graph.nNodes; ++node) {
    if (activeNode[node]) byRoot[components.find(node)].push_back(node);
  }
 
  // Deterministic component IDs make debugging and validation reproducible.
  std::vector<std::vector<std::size_t>> componentNodes;
  componentNodes.reserve(byRoot.size());
  for (auto& [_, nodes] : byRoot) {
    std::sort(nodes.begin(), nodes.end());
    componentNodes.push_back(std::move(nodes));
  }
  std::ranges::sort(componentNodes,
            [](const auto& first, const auto& second) {
              return first.front() < second.front();
            });
 
  xAOD::ContainerDecorator<xAOD::MuonSegmentContainer, std::vector<unsigned>>
      decor{m_pairGateDecorKey, ctx};
  xAOD::ContainerDecorator<xAOD::MuonSegmentContainer, std::vector<float>>
      scoreDecor{m_componentScoreDecorKey, ctx};
 
  std::vector<std::uint8_t> keptNode(graph.nNodes, false);
  std::vector<std::uint8_t> nodeDropReason;
  if (truthDiag) {
     nodeDropReason.assign(graph.nNodes, kReasonNotSelected);
  }
 
  // Group-purity: which kept-component id(s) each truth muon's
  // segments landed in, and whether any of those components were mixed
  std::unordered_map<int32_t, std::unordered_set<unsigned>> muonComponents;
  std::unordered_map<int32_t, bool> muonEverMixed;
  // D8: weak clean components and, per truth muon, the number of strong multi-layer components
  std::vector<int32_t> weakCleanMuons;
  std::unordered_map<int32_t, unsigned int> strongComponentsPerMuon;
  const auto sizeBin = [](std::size_t size) -> std::size_t {
    if (size <= 4) return size - 2;  // 2, 3, 4 -> 0, 1, 2
    if (size < 10) return 3;         // 5-9
    if (size < 20) return 4;         // 10-19
    return 5;                        // 20+
  };
 
  std::size_t topologyNodes = 0;
  std::size_t retainedNodes = 0;
  std::size_t chamberSuppressedNodes = 0;
  std::size_t rejectedComponents = 0;
  std::size_t componentsKept = 0;
  std::size_t anchors = 0;
  std::size_t nodesRejectedByMinComponent = 0;
  unsigned nextComponentId = 1;
  const InferenceUtils::SegmentQualityOrder betterSegment{};
 
  const auto isBetterNode = [&](std::size_t candidate, std::size_t incumbent) {
    if (betterSegment(graph.segments[candidate], graph.segments[incumbent])) {
      return true;
    }
    if (betterSegment(graph.segments[incumbent], graph.segments[candidate])) {
      return false;
    }
    return candidate < incumbent;
  };
 
  for (const std::vector<std::size_t>& rawNodes : componentNodes) {
    topologyNodes += rawNodes.size();
    std::vector<std::size_t> retained = rawNodes;
 
    // The seeder resolves same-chamber alternatives while building a seed.
    // Retaining only the highest-ranked representative is therefore optional.
    if (m_keepBestSegmentPerChamber.value()) {
      std::unordered_map<int, std::size_t> bestByChamber;
      bestByChamber.reserve(rawNodes.size());
      for (const std::size_t node : rawNodes) {
        const int chamber =
            static_cast<int>(graph.segments[node]->chamberIndex());
        const auto found = bestByChamber.find(chamber);
        if (found == bestByChamber.end() || isBetterNode(node, found->second)) {
          bestByChamber[chamber] = node;
        }
      }
      retained.clear();
      retained.reserve(bestByChamber.size());
      for (const auto& [_, node] : bestByChamber) retained.push_back(node);
      std::sort(retained.begin(), retained.end());
      chamberSuppressedNodes += rawNodes.size() - retained.size();
    }
 
    // Provisional reasons; nodes that end up kept are identified by keptNode.
    if (truthDiag) {
      for (const std::size_t node : rawNodes) {
          nodeDropReason[node] = kReasonChamberDedup;
      }
      for (const std::size_t node : retained) {
          nodeDropReason[node] = kReasonComponentRejected;
      }
    }
 
    if (retained.size() < m_minSegmentsPerComponent.value()) {
      nodesRejectedByMinComponent += retained.size();
      ++rejectedComponents;
      continue;
    }
 
    // Only ranked component members launch seeds. The edge score therefore
    // reduces seed attempts directly rather than serving only as a label.
    std::vector<std::size_t> rankedNodes{retained};
    if (m_anchorInnermostLayer.value()) {
      // Anchor only on the inner segment(s)
      int bestRank = std::numeric_limits<int>::max();
      for (const std::size_t node : rankedNodes) {
        bestRank = std::min(
            bestRank,
            layerRadialRank(Muon::MuonStationIndex::toLayerIndex(
                graph.segments[node]->chamberIndex())));
      }
      std::erase_if(rankedNodes, [&](std::size_t node) {
        return layerRadialRank(Muon::MuonStationIndex::toLayerIndex(
                   graph.segments[node]->chamberIndex())) != bestRank;
      });
      // Rank the inner anchors (strongest cross-layer edge first, segment quality as
      // tie-break) and cap them.
      if (m_seedAnchorsPerComponent.value() != 0) {
        std::ranges::sort(rankedNodes, [&](std::size_t first, std::size_t second) {
          const int order = InferenceUtils::compareFloatDescending(
              nodeBestCrossLayerScore[first], nodeBestCrossLayerScore[second]);
          return order != 0 ? order < 0 : isBetterNode(first, second);
        });
        if (rankedNodes.size() > m_seedAnchorsPerComponent.value()) {
          rankedNodes.resize(m_seedAnchorsPerComponent.value());
        }
      }
    } else {
      std::ranges::sort(rankedNodes, isBetterNode);
      const std::size_t nAnchors = m_seedAnchorsPerComponent.value() == 0
          ? rankedNodes.size()
          : std::min<std::size_t>(m_seedAnchorsPerComponent.value(),
                                  rankedNodes.size());
      rankedNodes.resize(nAnchors);
    }
    if (rankedNodes.empty()) {
      ++rejectedComponents;
      continue;
    }
    // rankedNodes stays in rank order (best anchor first): the decoration
    // carries the rank so that the seeder can try anchors best-first.
 
    const unsigned componentId = nextComponentId++;
    float componentCrossScore = -1.f;
    for (const std::size_t node : rawNodes) {
      componentCrossScore = std::max(componentCrossScore, nodeBestCrossLayerScore[node]);
    }
    for (const std::size_t node : retained) {
      scoreDecor(*graph.segments[node]) = {componentCrossScore, nodeBestCrossLayerScore[node]};
      const auto anchorIt = std::ranges::find(rankedNodes, node);
      const bool isAnchor = anchorIt != rankedNodes.end();
      decor(*graph.segments[node]) = {
          componentId,
          isAnchor ? static_cast<unsigned int>(anchorIt - rankedNodes.begin()) + 1 : 0u};
      keptNode[node] = true;
    }
    retainedNodes += retained.size();
    anchors += rankedNodes.size();
    ++componentsKept;
 
    // Group purity: does this kept segments from more than one truth muon?
    if (truthDiag) {
      std::unordered_set<int32_t> componentTruthIds;
      for (const std::size_t node : retained) {
        if (nodeTruthId[node] >= 0) componentTruthIds.insert(nodeTruthId[node]);
      }
      const std::size_t outcome = componentTruthIds.empty() ? 2
                                  : componentTruthIds.size() == 1 ? 0 : 1;  // clean/mixed/bkgOnly
      ++m_groupPurity.componentsByOutcome[outcome];
      ++m_groupPurity.componentSizeHist[sizeBin(retained.size())];
 
      // Component descriptors that a component-level cut could use: the
      // strongest selected edge among the nodes that formed it (before the
      // chamber dedup), its retained size and its number of distinct layers.
      float strongest = -1.f;
      for (const std::size_t node : rawNodes) {
        strongest = std::max(strongest, nodeBestSelectedScore[node]);
      }
      const std::size_t scoreBin = strongest < 0.05f ? 0 : strongest < 0.1f ? 1
                                 : strongest < 0.3f ? 2 : strongest < 0.5f ? 3 : 4;
      std::uint32_t layerMask = 0;
      for (const std::size_t node : retained) {
        // +1 so that LayerUnknown (-1) maps onto bit 0
        layerMask |= 1u << (Acts::toUnderlying(Muon::MuonStationIndex::toLayerIndex(
                                graph.segments[node]->chamberIndex())) + 1);
      }
      const std::size_t layerBin =
          std::min<std::size_t>(std::popcount(layerMask), GroupPurityCounters::kLayerBins) - 1;
      ++m_groupPurity.scoreByOutcome[outcome * GroupPurityCounters::kScoreBins + scoreBin];
      ++m_groupPurity.sizeByOutcome[outcome * GroupPurityCounters::kSizeBins + sizeBin(retained.size())];
      ++m_groupPurity.layersByOutcome[outcome * GroupPurityCounters::kLayerBins + layerBin];
 
      // Components that can seed (>= 2 layers)
      if (std::popcount(layerMask) >= 2) {
        float crossScore = -1.f;
        unsigned int crossEdges = 0;
        for (const std::size_t node : rawNodes) {
          crossScore = std::max(crossScore, nodeBestCrossLayerScore[node]);
          crossEdges += nodeCrossLayerEdges[node];
        }
        crossEdges /= 2;
        const std::size_t crossBin = crossScore < 0.05f ? 0 : crossScore < 0.1f ? 1 : crossScore < 0.3f ? 2
                                   : crossScore < 0.5f ? 3 : crossScore < 0.8f ? 4 : 5;
        ++m_groupPurity.crossScoreByOutcome[outcome * GroupPurityCounters::kCrossScoreBins + crossBin];
        ++m_groupPurity.crossEdgesByOutcome[outcome * GroupPurityCounters::kCrossEdgeBins +
                                            std::min<std::size_t>(crossEdges, 3)];
        if (crossScore < 0.05f) {
          if (outcome == 0) weakCleanMuons.push_back(*componentTruthIds.begin());
        } else {
          for (const int32_t id : componentTruthIds) ++strongComponentsPerMuon[id];
        }
      }
      const bool mixed = componentTruthIds.size() >= 2;
      if (mixed) {
        m_groupPurity.mixedComponentSegments += retained.size();
        const std::uint64_t packed =
            (static_cast<std::uint64_t>(componentTruthIds.size()) << 32) | retained.size();
        std::uint64_t current = m_groupPurity.worstMixedPacked.load(std::memory_order_relaxed);
        while (packed > current && !m_groupPurity.worstMixedPacked.compare_exchange_weak(
                   current, packed, std::memory_order_relaxed)) {}
      }
      for (const int32_t id : componentTruthIds) {
        muonComponents[id].insert(componentId);
        if (mixed) muonEverMixed[id] = true;
      }
    }
  }
 
  if (truthDiag) {
    std::size_t truthSegs = 0, bkgSegs = 0;
    for (std::size_t node = 0; node < graph.nNodes; ++node) {
      if (!keptNode[node]) continue;
      nodeTruthId[node] >= 0 ? ++truthSegs : ++bkgSegs;
    }
    m_sumRetainedTruthSegments += truthSegs;
    m_sumRetainedBkgSegments += bkgSegs;
 
    std::vector<unsigned char> thresholded(graph.nNodes, 0);
    for (std::size_t node = 0; node < graph.nNodes; ++node) {
      thresholded[node] = !edgesByNode[node].empty();
    }
    classifyLostTruthSegments(*segments, graph, pairProbability, thresholded,
                              keptNode, nodeDropReason);
    classifyGroupPurity(muonComponents, muonEverMixed, truthIdToAbsEta);
    for (const int32_t id : weakCleanMuons) {
      ++(strongComponentsPerMuon[id] > 0 ? m_groupPurity.weakCleanWithOther : m_groupPurity.weakCleanAlone);
    }
  }
 
  if (!m_filteredSegmentKey.empty()) {
    auto connectedSegments =
        std::make_unique<ConstDataVector<xAOD::MuonSegmentContainer>>(
            SG::VIEW_ELEMENTS);
    connectedSegments->reserve(graph.nNodes);
    for (std::size_t node = 0; node < graph.nNodes; ++node) {
      if (!keptNode[node] || !graph.segments[node]) continue;
      connectedSegments->push_back(graph.segments[node]);
    }
 
    const std::size_t nConnectedSegments = connectedSegments->size();
    SG::WriteHandle<ConstDataVector<xAOD::MuonSegmentContainer>> connectedHandle{
        m_filteredSegmentKey, ctx};
    ATH_CHECK(connectedHandle.record(std::move(connectedSegments)));
    ATH_MSG_DEBUG("Event " << ctx.eventID().event_number()
                  << ": wrote " << nConnectedSegments
                  << " ML-connected segment(s) to '"
                  << m_filteredSegmentKey.key() << "'");
  }
 
  ATH_MSG_DEBUG("Event " << ctx.eventID().event_number()
                << ": ML components graphNodes=" << graph.nNodes
                << ", thresholdedNodes=" << thresholdedNodes
                << ", thresholdPairs=" << thresholdPairs
                << ", mutualTopKPairs=" << mutualTopKPairs
                << ", oneSidedTopKPairs=" << oneSidedTopKPairs
                << ", selectedPairs=" << selectedPairKeys.size()
                << ", components=" << componentsKept
                << ", topologyNodes=" << topologyNodes
                << ", retainedNodes=" << retainedNodes
                << ", chamberSuppressedNodes=" << chamberSuppressedNodes
                << ", nodesRejectedByMinComponent=" << nodesRejectedByMinComponent
                << ", keepBestSegmentPerChamber=" << m_keepBestSegmentPerChamber.value()
                << ", seedAnchors=" << anchors
                << ", rejectedComponents=" << rejectedComponents
                << ", threshold=" << m_pairGateThreshold.value()
                << ", maxEdgesPerNode=" << m_maxEdgesPerNode.value()
                << ", orphanRecoveryPairs=" << orphanRecoveryPairs
                << ", mutualTopK=" << m_requireMutualTopKEdges.value()
                << ", degreeCapped=" << m_useDegreeCappedComponents.value());
 
  // Job-summed diagnostics
  if (msgLvl(MSG::DEBUG)) {
    m_sumGraphNodes += graph.nNodes;
    m_sumThresholdedNodes += thresholdedNodes;
    m_sumMutualTopKPairs += mutualTopKPairs;
    m_sumOneSidedTopKPairs += oneSidedTopKPairs;
    m_sumOrphanRecoveryPairs += orphanRecoveryPairs;
    m_sumTopologyNodes += topologyNodes;
    m_sumRetainedNodes += retainedNodes;
    m_sumChamberSuppressedNodes += chamberSuppressedNodes;
    m_sumNodesRejectedByMinComponent += nodesRejectedByMinComponent;
    m_sumRejectedComponents += rejectedComponents;
    m_sumComponentsKept += componentsKept;
  }
  return StatusCode::SUCCESS;
}

◆ extraDeps_update_handler()

void AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::extraDeps_update_handler ( Gaudi::Details::PropertyBase & ExtraDeps )

protectedinherited

Add StoreName to extra input/output deps as needed.

use the logic of the VarHandleKey to parse the DataObjID keys supplied via the ExtraInputs and ExtraOuputs Properties to add the StoreName if it's not explicitly given

◆ extraOutputDeps()

const DataObjIDColl & AthCommonAlgorithm< Gaudi::Algorithm >::extraOutputDeps ( ) const

overridevirtualinherited

Return the list of extra output dependencies.

This list is extended to include symlinks implied by inheritance relations.

Definition at line 89 of file AthCommonAlgorithm.cxx.

{
  // If we didn't find any symlinks to add, just return the collection
  // from the base class.  Otherwise, return the extended collection.
  if (!m_extendedExtraObjects.empty()) {
    return m_extendedExtraObjects;
  }
  return BaseAlg::extraOutputDeps();
}

◆ filterPassed()

virtual bool AthCommonAlgorithm< Gaudi::Algorithm >::filterPassed ( const EventContext & ctx ) const

inlinevirtualinherited

Get filter decision:

Definition at line 93 of file AthCommonAlgorithm.h.

                                                           {
    return execState( ctx ).filterPassed();
  }

◆ finalize()

StatusCode MuonML::SegmentEdgeInferenceAlg::finalize ( )

override

Log of the post-ONNX rank/mutuality and min-component-size selection.

Definition at line 935 of file SegmentEdgeInferenceAlg.cxx.

                                             {
  // The job-summed counters are only filled, and the summaries only built, at DEBUG level.
  if (!msgLvl(MSG::DEBUG)) return StatusCode::SUCCESS;
  ATH_MSG_DEBUG(
      "SegmentEdgeInferenceAlg post-ONNX selection summary (job-summed): "
      << "graphNodes=" << m_sumGraphNodes
      << ", thresholdedNodes=" << m_sumThresholdedNodes
      << " (>=PairGateThreshold=" << m_pairGateThreshold.value() << ")"
      << ", mutualTopKPairs=" << m_sumMutualTopKPairs
      << ", oneSidedTopKPairs(rejected by RequireMutualTopKEdges="
      << m_requireMutualTopKEdges.value() << ")=" << m_sumOneSidedTopKPairs
      << " (MaxEdgesPerNode=" << m_maxEdgesPerNode.value()
      << ", MaxEdgesPerNodePerLayer=" << m_maxEdgesPerNodePerLayer.value() << " (0 = global top-K)"
      << "; a thresholded edge is dropped here purely by rank, "
         "independent of PairGateThreshold)"
      << ", orphanRecoveryPairs=" << m_sumOrphanRecoveryPairs
      << ", chamberSuppressedNodes=" << m_sumChamberSuppressedNodes
      << " (KeepBestSegmentPerChamber=" << m_keepBestSegmentPerChamber.value() << ")"
      << ", nodesRejectedByMinComponent=" << m_sumNodesRejectedByMinComponent
      << " (MinSegmentsPerComponent=" << m_minSegmentsPerComponent.value() << ")"
      << ", rejectedComponents=" << m_sumRejectedComponents
      << ", componentsKept=" << m_sumComponentsKept
      << ", topologyNodes=" << m_sumTopologyNodes
      << ", retainedNodes=" << m_sumRetainedNodes);
 
  if (m_truthDiagEnabled) {
    ATH_MSG_DEBUG("True cross-layer edges above PairGateThreshold kept by the selection, selected/total: barrel="
                  << m_sumTrueCrossLayerSelected[0] << "/" << m_sumTrueCrossLayerEdges[0]
                  << ", endcap=" << m_sumTrueCrossLayerSelected[1] << "/" << m_sumTrueCrossLayerEdges[1]);
    ATH_MSG_DEBUG(
        "SegmentEdgeInferenceAlg truth-vs-background funnel (job-summed; "
        "truth segment = getTruthMatchedParticle(seg) != nullptr, matching "
        "SegmentDumperAlg::m_segmentHasTruth, no isMuon() filter, no G4 "
        "pseudo-label fallback): "
        << "input: truth=" << m_sumInputTruthSegments
        << " bkg=" << m_sumInputBkgSegments
        << "; reachesONNX: truth=" << m_sumOnnxTruthSegments
        << " bkg=" << m_sumOnnxBkgSegments
        << "; retained: truth=" << m_sumRetainedTruthSegments
        << " bkg=" << m_sumRetainedBkgSegments
        << "; edge-level (same truth-particle index on both endpoints) at "
           "PairGateThreshold=" << m_pairGateThreshold.value()
        << ": trueEdges passed/total=" << m_sumTrueEdgesPassed << "/"
        << m_sumTrueEdgesTotal
        << ", bkgEdges passed/total=" << m_sumBkgEdgesPassed << "/"
        << m_sumBkgEdgesTotal);
 
    constexpr std::array<const char*, TruthLossCounters::kCategories> names{
        "A1 no possible partner: singleton muon segment",
        "A2 no possible partner: same-chamber partners only",
        "B0 never reached ONNX: segment dropped by MaxSegmentsPerBucket",
        "B1 never reached ONNX: partners dropped by MaxSegmentsPerBucket",
        "B2 never reached ONNX: outside sector window (MaxDeltaSector)",
        "B3 never reached ONNX: outside angle window (MaxDeltaThetaDeg)",
        "B4 never reached ONNX: pre-ONNX edge caps",
        "C  true pair(s) scored below PairGateThreshold",
        "D1 passed threshold, removed: KeepBestSegmentPerChamber",
        "D2 passed threshold, removed: component rejected (MinSegmentsPerComponent/anchors)",
        "D3 passed threshold, removed: not selected by top-K/mutual gate"};
    constexpr std::size_t kColumns = TruthLossCounters::kColumns;
    ATH_MSG_DEBUG("Lost truth segments by stage (job-summed; truth segment = "
                  "input segment with a truth particle, lost = not in the filtered "
                  "container). Columns: all | barrel | transition | endcap | "
                  "belonging to a muon that lost seedability");
    std::size_t lostTotal = 0;
    for (std::size_t c = 0; c < names.size(); ++c) {
      const auto value = [&](std::size_t col) { return m_truthLoss.category[c * kColumns + col].load(); };
      lostTotal += value(0);
      ATH_MSG_DEBUG("  " << names[c] << ": " << value(0) << " | " << value(1)
                    << " | " << value(2) << " | " << value(3) << " | " << value(4));
    }
    ATH_MSG_DEBUG("  check: sum of categories=" << lostTotal
                  << ", input truth - retained truth="
                  << (m_sumInputTruthSegments.load() - m_sumRetainedTruthSegments.load()));
    ATH_MSG_DEBUG("  truth muons with >=2 input chambers (all|barrel|transition|endcap): "
                  << m_truthLoss.muonsSeedable[0].load() << "|" << m_truthLoss.muonsSeedable[1].load()
                  << "|" << m_truthLoss.muonsSeedable[2].load() << "|" << m_truthLoss.muonsSeedable[3].load()
                  << "; of which <2 chambers retained: "
                  << m_truthLoss.muonsSeedLost[0].load() << "|" << m_truthLoss.muonsSeedLost[1].load()
                  << "|" << m_truthLoss.muonsSeedLost[2].load() << "|" << m_truthLoss.muonsSeedLost[3].load());
    ATH_MSG_DEBUG("  category C descriptors: best true-pair score <1e-3/1e-3..1e-2/1e-2..2e-2/2e-2..5e-2/>=5e-2: "
                  << m_truthLoss.scoreBin[0].load() << "/" << m_truthLoss.scoreBin[1].load() << "/"
                  << m_truthLoss.scoreBin[2].load() << "/" << m_truthLoss.scoreBin[3].load() << "/"
                  << m_truthLoss.scoreBin[4].load()
                  << "; pair sector delta same/adjacent: " << m_truthLoss.sectorDelta[0].load() << "/"
                  << m_truthLoss.sectorDelta[1].load()
                  << "; layer pair Inner-Middle/Middle-Outer/Inner-Outer/other: "
                  << m_truthLoss.layerPair[0].load() << "/" << m_truthLoss.layerPair[1].load() << "/"
                  << m_truthLoss.layerPair[2].load() << "/" << m_truthLoss.layerPair[3].load()
                  << "; muon input segments 2/3/4/5+: " << m_truthLoss.muonSegments[0].load() << "/"
                  << m_truthLoss.muonSegments[1].load() << "/" << m_truthLoss.muonSegments[2].load() << "/"
                  << m_truthLoss.muonSegments[3].load()
                  << "; nPrecisionHits <=4/5-6/>=7: " << m_truthLoss.precisionHits[0].load() << "/"
                  << m_truthLoss.precisionHits[1].load() << "/" << m_truthLoss.precisionHits[2].load());
 
    ATH_MSG_DEBUG(
        "SegmentEdgeInferenceAlg grouping purity (job-summed; components cover "
        "only kept components, i.e. those that survived MinSegmentsPerComponent "
        "and appear in the output): "
        << "clean=" << m_groupPurity.componentsByOutcome[0].load()
        << " mixed=" << m_groupPurity.componentsByOutcome[1].load()
        << " bkgOnly=" << m_groupPurity.componentsByOutcome[2].load()
        << "; segments inside mixed components=" << m_groupPurity.mixedComponentSegments.load()
        << "; component size histogram (2/3/4/5-9/10-19/20+): "
        << m_groupPurity.componentSizeHist[0].load() << "/" << m_groupPurity.componentSizeHist[1].load() << "/"
        << m_groupPurity.componentSizeHist[2].load() << "/" << m_groupPurity.componentSizeHist[3].load() << "/"
        << m_groupPurity.componentSizeHist[4].load() << "/" << m_groupPurity.componentSizeHist[5].load());
 
    // Kept components per outcome, binned by a candidate component-level cut variable.
    const auto printByOutcome = [&](const char* label, const auto& counters, std::size_t nBins) {
      constexpr std::array<const char*, 3> outcomes{"clean", "mixed", "bkgOnly"};
      std::stringstream sstr{};
      for (std::size_t outcome = 0; outcome < outcomes.size(); ++outcome) {
        sstr << " " << outcomes[outcome] << "=";
        for (std::size_t bin = 0; bin < nBins; ++bin) {
          sstr << (bin ? "/" : "") << counters[outcome * nBins + bin].load();
        }
      }
      ATH_MSG_DEBUG("  kept components by " << label << ":" << sstr.str());
    };
    printByOutcome("strongest selected-edge score (<0.05/0.05-0.1/0.1-0.3/0.3-0.5/>=0.5)",
                   m_groupPurity.scoreByOutcome, GroupPurityCounters::kScoreBins);
    printByOutcome("retained segments (2/3/4/5-9/10-19/20+)",
                   m_groupPurity.sizeByOutcome, GroupPurityCounters::kSizeBins);
    printByOutcome("distinct layers (1/2/3/4+)",
                   m_groupPurity.layersByOutcome, GroupPurityCounters::kLayerBins);
    printByOutcome("strongest selected cross-layer edge, components with >=2 layers "
                   "(<0.05/0.05-0.1/0.1-0.3/0.3-0.5/0.5-0.8/>=0.8)",
                   m_groupPurity.crossScoreByOutcome, GroupPurityCounters::kCrossScoreBins);
    printByOutcome("number of selected cross-layer edges, components with >=2 layers (0/1/2/3+)",
                   m_groupPurity.crossEdgesByOutcome, GroupPurityCounters::kCrossEdgeBins);
    ATH_MSG_DEBUG("  D8 clean components with >=2 layers and strongest cross-layer edge < 0.05: "
                  << "their muon has another component with >=2 layers and a stronger edge="
                  << m_groupPurity.weakCleanWithOther.load()
                  << ", it is the muon's only such component=" << m_groupPurity.weakCleanAlone.load());
 
    const std::uint64_t worstMixed = m_groupPurity.worstMixedPacked.load();
    ATH_MSG_DEBUG("  worst mixed component this job: "
                  << (worstMixed >> 32) << " distinct truth muons in "
                  << (worstMixed & 0xffffffffull) << " segments"
                  << (worstMixed == 0 ? " (no mixed components found)" : ""));
 
    ATH_MSG_DEBUG(
        "  truth-muon grouping outcome (all|barrel|transition|endcap; muons with "
        ">=1 retained segment only, fully-lost muons excluded -- see muonsSeedLost above): "
        << "clean=" << m_groupPurity.muonOutcome[0 * 3 + 0].load() << "|"
        << m_groupPurity.muonOutcome[1 * 3 + 0].load() << "|"
        << m_groupPurity.muonOutcome[2 * 3 + 0].load() << "|"
        << m_groupPurity.muonOutcome[3 * 3 + 0].load()
        << "; mixed=" << m_groupPurity.muonOutcome[0 * 3 + 1].load() << "|"
        << m_groupPurity.muonOutcome[1 * 3 + 1].load() << "|"
        << m_groupPurity.muonOutcome[2 * 3 + 1].load() << "|"
        << m_groupPurity.muonOutcome[3 * 3 + 1].load()
        << "; fragmented=" << m_groupPurity.muonOutcome[0 * 3 + 2].load() << "|"
        << m_groupPurity.muonOutcome[1 * 3 + 2].load() << "|"
        << m_groupPurity.muonOutcome[2 * 3 + 2].load() << "|"
        << m_groupPurity.muonOutcome[3 * 3 + 2].load());
  }
  return StatusCode::SUCCESS;
}

◆ initialize()

StatusCode MuonML::SegmentEdgeInferenceAlg::initialize ( )

override

Definition at line 118 of file SegmentEdgeInferenceAlg.cxx.

                                               {
  ATH_CHECK(m_segmentKey.initialize());
  ATH_CHECK(m_pairGateDecorKey.initialize());
  ATH_CHECK(m_componentScoreDecorKey.initialize());
  ATH_CHECK(m_filteredSegmentKey.initialize(!m_filteredSegmentKey.empty()));
  ATH_CHECK(m_edgeClassifier.retrieve());
  m_truthDiagEnabled = m_edgeClassifier->enableTruthDiagnostics();
  return StatusCode::SUCCESS;
}

◆ inputHandles()

virtual std::vector< Gaudi::DataHandle * > AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::inputHandles ( ) const

overridevirtualinherited

Return this algorithm's input handles.

We override this to include handle instances from key arrays if they have not yet been declared. See comments on updateVHKA.

◆ isClonable()

virtual bool AthCommonAlgorithm< Gaudi::Algorithm >::isClonable ( ) const

inlineoverridevirtualinherited

Specify if the algorithm is clonable.

Only relevant for non-reentrant algorithms. Actual number of clones needs to be set via the "Cardinality" property.

Reimplemented in AFP_DigiTop, AlgB, AlgT, BCM_Digitization, CscDigitBuilder, CscDigitToCscRDO, G4AtlasAlg, G4RunAlg, HGTD_Digitization, HiveAlgBase, InDet::GNNSeedingTrackMaker, InDet::SCT_Clusterization, InDet::SiSPGNNTrackMaker, InDet::SiSPSeededTrackFinder, InDet::SiTrackerSpacePointFinder, ISF::SimKernelMT, ITk::StripDigitization, ITkPixelCablingAlg, ITkStripCablingAlg, LArHitEMapMaker, LArTTL1Maker, LUCID_DigiTop, LVL1::L1TopoSimulation, MergeCalibHits, MergeGenericMuonSimHitColl, MergeHijingPars, MergeMcEventCollection, MergeTrackRecordCollection, MergeTruthJets, MergeTruthParticles, MuonDigitizer, PileUpMTAlg, PixelDigitization, RoIBResultToxAOD, SCT_ByteStreamErrorsTestAlg, SCT_CablingCondAlgFromCoraCool, SCT_CablingCondAlgFromText, SCT_ConditionsParameterTestAlg, SCT_ConditionsSummaryTestAlg, SCT_ConfigurationConditionsTestAlg, SCT_Digitization, SCT_FlaggedConditionTestAlg, SCT_LinkMaskingTestAlg, SCT_MajorityConditionsTestAlg, SCT_ModuleVetoTestAlg, SCT_MonitorConditionsTestAlg, SCT_PrepDataToxAOD, SCT_RawDataToxAOD, SCT_ReadCalibChipDataTestAlg, SCT_ReadCalibDataTestAlg, SCT_RODVetoTestAlg, SCT_SensorsTestAlg, SCT_SiliconConditionsTestAlg, SCT_StripVetoTestAlg, SCT_TdaqEnabledTestAlg, SCT_TestCablingAlg, SCTEventFlagWriter, SCTRawDataProvider, SCTSiLorentzAngleTestAlg, SCTSiPropertiesTestAlg, SGInputLoader, Simulation::BeamEffectsAlg, TileHitVecToCnt, TileMuonFitter, TilePulseForTileMuonReceiver, TileRawChannelMaker, TRTDigitization, and ZDC_DigiTop.

Definition at line 68 of file AthCommonAlgorithm.h.

                                           {
    return true;
  }

◆ msg()

MsgStream & AthCommonMsg< Gaudi::Algorithm >::msg ( ) const

inlineinherited

Definition at line 24 of file AthCommonMsg.h.

                                {
    return this->msgStream();
  }

◆ msgLvl()

bool AthCommonMsg< Gaudi::Algorithm >::msgLvl ( const MSG::Level lvl ) const

inlineinherited

Definition at line 30 of file AthCommonMsg.h.

                                               {
    return this->msgLevel(lvl);
  }

◆ outputHandles()

virtual std::vector< Gaudi::DataHandle * > AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::outputHandles ( ) const

overridevirtualinherited

Return this algorithm's output handles.

We override this to include handle instances from key arrays if they have not yet been declared. See comments on updateVHKA.

◆ renounce()

std::enable_if_t< std::is_void_v< std::result_of_t< decltype(&T::renounce)(T)> > &&!std::is_base_of_v< SG::VarHandleKeyArray, T > &&std::is_base_of_v< Gaudi::DataHandle, T >, void > AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::renounce ( T & h )

inlineprotectedinherited

Definition at line 368 of file AthCommonDataStore.h.

  {
    h.renounce();
    PBASE::renounce (h);
  }

◆ renounceArray()

void AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::renounceArray ( SG::VarHandleKeyArray & handlesArray )

inlineprotectedinherited

remove all handles from I/O resolution

Definition at line 352 of file AthCommonDataStore.h.

                                                          {
    handlesArray.renounce();
  }

◆ setFilterPassed()

virtual void AthCommonAlgorithm< Gaudi::Algorithm >::setFilterPassed	(	bool	state,
		const EventContext &	ctx ) const

inlinevirtualinherited

Set filter decision:

Definition at line 99 of file AthCommonAlgorithm.h.

                                                                            {
    execState( ctx ).setFilterPassed( state );
  }

◆ sysExecute()

StatusCode AthCommonAlgorithm< Gaudi::Algorithm >::sysExecute ( const EventContext & ctx )

overridevirtualinherited

Execute an algorithm.

We override this in order to work around an issue with the Algorithm base class storing the event context in a member variable that can cause crashes in MT jobs.

Reimplemented in AthAnalysisAlgorithm.

Definition at line 80 of file AthCommonAlgorithm.cxx.

{
  return BaseAlg::sysExecute (ctx);
}

◆ sysInitialize()

StatusCode AthCommonAlgorithm< Gaudi::Algorithm >::sysInitialize ( )

overridevirtualinherited

Override sysInitialize.

Override sysInitialize from the base class.

Loop through all output handles, and if they're WriteCondHandles, automatically register them and this Algorithm with the CondSvc

Scan through all outputHandles, and if they're WriteCondHandles, register them with the CondSvc

Reimplemented from AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >.

Reimplemented in AthAnalysisAlgorithm, AthHistogramAlgorithm, HypoBase, InputMakerBase, and PyAthena::Alg.

Definition at line 60 of file AthCommonAlgorithm.cxx.

                                                      {
  StatusCode sc=AthCommonDataStore<AthCommonMsg<BaseAlg>>::sysInitialize();
 
  if (sc.isFailure()) {
    return sc;
  }
  
  ServiceHandle<ICondSvc> cs("CondSvc",name());
  for (auto h : outputHandles()) {
    if (h->isCondition() && h->mode() == Gaudi::DataHandle::Writer) {
      // do this inside the loop so we don't create the CondSvc until needed
      if ( cs.retrieve().isFailure() ) {
        ATH_MSG_WARNING("no CondSvc found: won't autoreg WriteCondHandles");
        return StatusCode::SUCCESS;
      }      
      if (cs->regHandle(this,*h).isFailure()) {
        sc = StatusCode::FAILURE;
        ATH_MSG_ERROR("unable to register WriteCondHandle " << h->fullKey()
                      << " with CondSvc");
      }
    }
  }
  return sc;  
}

◆ sysStart()

virtual StatusCode AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::sysStart ( )

overridevirtualinherited

Handle START transition.

We override this in order to make sure that conditions handle keys can cache a pointer to the conditions container.

◆ updateVHKA()

void AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::updateVHKA ( Gaudi::Details::PropertyBase & )

inlineinherited

Definition at line 298 of file AthCommonDataStore.h.

                                               {
    for (auto &a : m_vhka) {
      std::vector<SG::VarHandleKey*> keys = a->keys();
      for (auto k : keys) {
        k->setOwner(this);
      }
    }
  }

Member Data Documentation

◆ ATLAS_THREAD_SAFE [1/4]

GroupPurityCounters m_groupPurity MuonML::SegmentEdgeInferenceAlg::ATLAS_THREAD_SAFE

mutableprivate

Every element is a std::atomic, updated only with atomic additions/CAS.

Definition at line 184 of file SegmentEdgeInferenceAlg.h.

◆ ATLAS_THREAD_SAFE [2/4]

TruthLossCounters m_truthLoss MuonML::SegmentEdgeInferenceAlg::ATLAS_THREAD_SAFE

mutableprivate

Every element is a std::atomic, updated only with atomic additions.

Definition at line 154 of file SegmentEdgeInferenceAlg.h.

◆ ATLAS_THREAD_SAFE [3/4]

std::array<std::atomic<std::size_t>, 2> m_sumTrueCrossLayerSelected MuonML::SegmentEdgeInferenceAlg::ATLAS_THREAD_SAFE {}

mutableprivate

Definition at line 134 of file SegmentEdgeInferenceAlg.h.

134{};

◆ ATLAS_THREAD_SAFE [4/4]

std::array<std::atomic<std::size_t>, 2> m_sumTrueCrossLayerEdges MuonML::SegmentEdgeInferenceAlg::ATLAS_THREAD_SAFE {}

mutableprivate

True cross-layer edges above PairGateThreshold, total and kept by the selection; barrel / endcap Every element is a std::atomic, updated only with atomic increments.

Definition at line 133 of file SegmentEdgeInferenceAlg.h.

133{};

◆ m_anchorInnermostLayer

Gaudi::Property<bool> MuonML::SegmentEdgeInferenceAlg::m_anchorInnermostLayer

private

Initial value:

{
        this, "AnchorInnermostLayer", false,
        "Restrict seed anchors to inner segment(s); with SeedAnchorsPerComponent > 0 keep only "
        "the inner segment(s) with the strongest selected edge towards another layer"}

Definition at line 89 of file SegmentEdgeInferenceAlg.h.

                                              {
        this, "AnchorInnermostLayer", false,
        "Restrict seed anchors to inner segment(s); with SeedAnchorsPerComponent > 0 keep only "
        "the inner segment(s) with the strongest selected edge towards another layer"};

◆ m_componentScoreDecorKey

SG::WriteDecorHandleKey<xAOD::MuonSegmentContainer> MuonML::SegmentEdgeInferenceAlg::m_componentScoreDecorKey

private

Initial value:

{

this, "ComponentScoreDecoration", "MsSegments.mlComponentScore"}

Per-segment ML scores for MlMsTrackSeeder: [strongest selected cross-layer edge of the segment's component, strongest selected cross-layer edge of the segment itself] (-1 if none).

Written for the segments of kept components.

Definition at line 60 of file SegmentEdgeInferenceAlg.h.

60 {

61 this, "ComponentScoreDecoration", "MsSegments.mlComponentScore"};

◆ m_detStore

StoreGateSvc_t AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::m_detStore

privateinherited

Pointer to StoreGate (detector store by default).

Definition at line 381 of file AthCommonDataStore.h.

◆ m_edgeClassifier

ToolHandle<ISegmentEdgeClassifierTool> MuonML::SegmentEdgeInferenceAlg::m_edgeClassifier {this, "EdgeClassifierTool", "MuonML::SegmentEdgeClassifierTool/SegmentEdgeClassifierTool"}

private

Definition at line 99 of file SegmentEdgeInferenceAlg.h.

99{this, "EdgeClassifierTool", "MuonML::SegmentEdgeClassifierTool/SegmentEdgeClassifierTool"};

◆ m_evtStore

StoreGateSvc_t AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::m_evtStore

privateinherited

Pointer to StoreGate (event store by default).

Definition at line 378 of file AthCommonDataStore.h.

◆ m_extendedExtraObjects

DataObjIDColl AthCommonAlgorithm< Gaudi::Algorithm >::m_extendedExtraObjects

privateinherited

Extra output dependency collection, extended by AthAlgorithmDHUpdate to add symlinks.

Empty if no symlinks were found.

Definition at line 108 of file AthCommonAlgorithm.h.

◆ m_filteredSegmentKey

SG::WriteHandleKey<ConstDataVector<xAOD::MuonSegmentContainer> > MuonML::SegmentEdgeInferenceAlg::m_filteredSegmentKey

private

Initial value:

{
        this, "FilteredSegmentKey", "",
        "Optional VIEW container containing only segments incident to a "
        "post-classifier selected edge; empty disables the output"}

Definition at line 62 of file SegmentEdgeInferenceAlg.h.

                                                                                    {
        this, "FilteredSegmentKey", "",
        "Optional VIEW container containing only segments incident to a "
        "post-classifier selected edge; empty disables the output"};

◆ m_keepBestSegmentPerChamber

Gaudi::Property<bool> MuonML::SegmentEdgeInferenceAlg::m_keepBestSegmentPerChamber

private

Initial value:

{
        this, "KeepBestSegmentPerChamber", true,
        "Keep only the ML-best segment in each chamber within a component"}

Definition at line 93 of file SegmentEdgeInferenceAlg.h.

                                                   {
        this, "KeepBestSegmentPerChamber", true,
        "Keep only the ML-best segment in each chamber within a component"};

◆ m_maxEdgesPerNode

Gaudi::Property<unsigned int> MuonML::SegmentEdgeInferenceAlg::m_maxEdgesPerNode

private

Initial value:

{
        this, "MaxEdgesPerNode", 2,
        "Keep at most this many highest-score neighbours per segment when forming the ML path graph; zero keeps all"}

Definition at line 69 of file SegmentEdgeInferenceAlg.h.

                                                 {
        this, "MaxEdgesPerNode", 2,
        "Keep at most this many highest-score neighbours per segment when forming the ML path graph; zero keeps all"};

◆ m_maxEdgesPerNodePerLayer

Gaudi::Property<unsigned int> MuonML::SegmentEdgeInferenceAlg::m_maxEdgesPerNodePerLayer

private

Initial value:

{
        this, "MaxEdgesPerNodePerLayer", 0,
        "If > 0, each segment nominates its this-many highest-score edges towards EACH layer (Inner, "
        "Middle, Outer, Extended, ...) instead of its MaxEdgesPerNode best edges overall; zero keeps "
        "the global top-K"}

Definition at line 72 of file SegmentEdgeInferenceAlg.h.

                                                         {
        this, "MaxEdgesPerNodePerLayer", 0,
        "If > 0, each segment nominates its this-many highest-score edges towards EACH layer (Inner, "
        "Middle, Outer, Extended, ...) instead of its MaxEdgesPerNode best edges overall; zero keeps "
        "the global top-K"};

◆ m_minSegmentsPerComponent

Gaudi::Property<unsigned int> MuonML::SegmentEdgeInferenceAlg::m_minSegmentsPerComponent

private

Initial value:

{
        this, "MinSegmentsPerComponent", 2,
        "Discard ML components with fewer retained segments"}

Definition at line 96 of file SegmentEdgeInferenceAlg.h.

                                                         {
        this, "MinSegmentsPerComponent", 2,
        "Discard ML components with fewer retained segments"};

◆ m_pairGateDecorKey

SG::WriteDecorHandleKey<xAOD::MuonSegmentContainer> MuonML::SegmentEdgeInferenceAlg::m_pairGateDecorKey

private

Initial value:

{

this, "PairGateDecoration", "MsSegments.mlTrackComponent"}

Per-segment payload consumed by MlMsTrackSeeder: [componentId, anchorRank + 1] Empty means that the segment was rejected by the ML preselection.

Definition at line 55 of file SegmentEdgeInferenceAlg.h.

55 {

56 this, "PairGateDecoration", "MsSegments.mlTrackComponent"};

◆ m_pairGateThreshold

Gaudi::Property<float> MuonML::SegmentEdgeInferenceAlg::m_pairGateThreshold

private

Initial value:

{
        this, "PairGateThreshold", 0.5f,
        "Minimum edge probability used to form ML track components"}

Definition at line 66 of file SegmentEdgeInferenceAlg.h.

                                            {
        this, "PairGateThreshold", 0.5f,
        "Minimum edge probability used to form ML track components"};

◆ m_recoverOrphanNodes

Gaudi::Property<bool> MuonML::SegmentEdgeInferenceAlg::m_recoverOrphanNodes

private

Initial value:

{
        this, "RecoverOrphanNodes", true,
        "For a thresholded node without a mutual top-K edge, retain its best one-sided top-K association"}

Definition at line 83 of file SegmentEdgeInferenceAlg.h.

                                            {
        this, "RecoverOrphanNodes", true,
        "For a thresholded node without a mutual top-K edge, retain its best one-sided top-K association"};

◆ m_requireMutualTopKEdges

Gaudi::Property<bool> MuonML::SegmentEdgeInferenceAlg::m_requireMutualTopKEdges

private

Initial value:

{
        this, "RequireMutualTopKEdges", true,
        "Retain an ML association only when both endpoint segments rank it in their top MaxEdgesPerNode scores"}

Definition at line 80 of file SegmentEdgeInferenceAlg.h.

                                                {
        this, "RequireMutualTopKEdges", true,
        "Retain an ML association only when both endpoint segments rank it in their top MaxEdgesPerNode scores"};

◆ m_seedAnchorsPerComponent

Gaudi::Property<unsigned int> MuonML::SegmentEdgeInferenceAlg::m_seedAnchorsPerComponent

private

Initial value:

{
        this, "SeedAnchorsPerComponent", 0,
        "Number of highest-score segments that may seed each ML component; zero keeps every retained segment"}

Definition at line 86 of file SegmentEdgeInferenceAlg.h.

                                                         {
        this, "SeedAnchorsPerComponent", 0,
        "Number of highest-score segments that may seed each ML component; zero keeps every retained segment"};

◆ m_segmentKey

SG::ReadHandleKey<xAOD::MuonSegmentContainer> MuonML::SegmentEdgeInferenceAlg::m_segmentKey {this, "SegmentKey", "MsSegments"}

private

Definition at line 52 of file SegmentEdgeInferenceAlg.h.

52{this, "SegmentKey", "MsSegments"};

◆ m_sumBkgEdgesPassed

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumBkgEdgesPassed {0}

mutableprivate

Definition at line 136 of file SegmentEdgeInferenceAlg.h.

136{0};

◆ m_sumBkgEdgesTotal

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumBkgEdgesTotal {0}

mutableprivate

Definition at line 135 of file SegmentEdgeInferenceAlg.h.

135{0};

◆ m_sumChamberSuppressedNodes

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumChamberSuppressedNodes {0}

mutableprivate

Definition at line 113 of file SegmentEdgeInferenceAlg.h.

113{0};

◆ m_sumComponentsKept

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumComponentsKept {0}

mutableprivate

Definition at line 116 of file SegmentEdgeInferenceAlg.h.

116{0};

◆ m_sumGraphNodes

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumGraphNodes {0}

mutableprivate

Job-summed post-ONNX selection counters (see execute()).

Definition at line 106 of file SegmentEdgeInferenceAlg.h.

106{0};

◆ m_sumInputBkgSegments

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumInputBkgSegments {0}

mutableprivate

Definition at line 124 of file SegmentEdgeInferenceAlg.h.

124{0};

◆ m_sumInputTruthSegments

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumInputTruthSegments {0}

mutableprivate

Definition at line 123 of file SegmentEdgeInferenceAlg.h.

123{0};

◆ m_sumMutualTopKPairs

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumMutualTopKPairs {0}

mutableprivate

Definition at line 108 of file SegmentEdgeInferenceAlg.h.

108{0};

◆ m_sumNodesRejectedByMinComponent

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumNodesRejectedByMinComponent {0}

mutableprivate

Definition at line 114 of file SegmentEdgeInferenceAlg.h.

114{0};

◆ m_sumOneSidedTopKPairs

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumOneSidedTopKPairs {0}

mutableprivate

Definition at line 109 of file SegmentEdgeInferenceAlg.h.

109{0};

◆ m_sumOnnxBkgSegments

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumOnnxBkgSegments {0}

mutableprivate

Definition at line 126 of file SegmentEdgeInferenceAlg.h.

126{0};

◆ m_sumOnnxTruthSegments

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumOnnxTruthSegments {0}

mutableprivate

Definition at line 125 of file SegmentEdgeInferenceAlg.h.

125{0};

◆ m_sumOrphanRecoveryPairs

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumOrphanRecoveryPairs {0}

mutableprivate

Definition at line 110 of file SegmentEdgeInferenceAlg.h.

110{0};

◆ m_sumRejectedComponents

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumRejectedComponents {0}

mutableprivate

Definition at line 115 of file SegmentEdgeInferenceAlg.h.

115{0};

◆ m_sumRetainedBkgSegments

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumRetainedBkgSegments {0}

mutableprivate

Definition at line 128 of file SegmentEdgeInferenceAlg.h.

128{0};

◆ m_sumRetainedNodes

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumRetainedNodes {0}

mutableprivate

Definition at line 112 of file SegmentEdgeInferenceAlg.h.

112{0};

◆ m_sumRetainedTruthSegments

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumRetainedTruthSegments {0}

mutableprivate

Definition at line 127 of file SegmentEdgeInferenceAlg.h.

127{0};

◆ m_sumThresholdedNodes

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumThresholdedNodes {0}

mutableprivate

Definition at line 107 of file SegmentEdgeInferenceAlg.h.

107{0};

◆ m_sumTopologyNodes

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumTopologyNodes {0}

mutableprivate

Definition at line 111 of file SegmentEdgeInferenceAlg.h.

111{0};

◆ m_sumTrueEdgesPassed

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumTrueEdgesPassed {0}

mutableprivate

Definition at line 130 of file SegmentEdgeInferenceAlg.h.

130{0};

◆ m_sumTrueEdgesTotal

std::atomic<std::size_t> MuonML::SegmentEdgeInferenceAlg::m_sumTrueEdgesTotal {0}

mutableprivate

Definition at line 129 of file SegmentEdgeInferenceAlg.h.

129{0};

◆ m_truthDiagEnabled

bool MuonML::SegmentEdgeInferenceAlg::m_truthDiagEnabled {false}

private

Whether truth-particle matching is available/enabled this job.

Definition at line 103 of file SegmentEdgeInferenceAlg.h.

103{false};

◆ m_useDegreeCappedComponents

Gaudi::Property<bool> MuonML::SegmentEdgeInferenceAlg::m_useDegreeCappedComponents

private

Initial value:

{
        this, "UseDegreeCappedComponents", false,
        "Use a global greedy degree cap instead of the mutual top-K path graph"}

Definition at line 77 of file SegmentEdgeInferenceAlg.h.

                                                   {
        this, "UseDegreeCappedComponents", false,
        "Use a global greedy degree cap instead of the mutual top-K path graph"};

◆ m_varHandleArraysDeclared

bool AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::m_varHandleArraysDeclared

privateinherited

Definition at line 387 of file AthCommonDataStore.h.

◆ m_vhka

std::vector<SG::VarHandleKeyArray*> AthCommonDataStore< AthCommonMsg< Gaudi::Algorithm > >::m_vhka

privateinherited

Definition at line 386 of file AthCommonDataStore.h.

◆ m_warnedMissingToolDiagnostics

std::atomic<bool> MuonML::SegmentEdgeInferenceAlg::m_warnedMissingToolDiagnostics {false}

mutableprivate

Definition at line 155 of file SegmentEdgeInferenceAlg.h.

155{false};

The documentation for this class was generated from the following files:

Classes

Public Member Functions

Protected Member Functions

Private Types

Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

◆ StoreGateSvc_t

Member Function Documentation

◆ classifyGroupPurity()

◆ classifyLostTruthSegments()

◆ declareGaudiProperty()

◆ declareProperty()

◆ detStore()

◆ evtStore()

◆ execute()

◆ extraDeps_update_handler()

◆ extraOutputDeps()

◆ filterPassed()

◆ finalize()

◆ initialize()

◆ inputHandles()

◆ isClonable()

◆ msg()

◆ msgLvl()

◆ outputHandles()

◆ renounce()

◆ renounceArray()

◆ setFilterPassed()

◆ sysExecute()

◆ sysInitialize()

◆ sysStart()

◆ updateVHKA()

Member Data Documentation

◆ ATLAS_THREAD_SAFE [1/4]

◆ ATLAS_THREAD_SAFE [2/4]

◆ ATLAS_THREAD_SAFE [3/4]

◆ ATLAS_THREAD_SAFE [4/4]

◆ m_anchorInnermostLayer

◆ m_componentScoreDecorKey

◆ m_detStore

◆ m_edgeClassifier

◆ m_evtStore

◆ m_extendedExtraObjects

◆ m_filteredSegmentKey

◆ m_keepBestSegmentPerChamber

◆ m_maxEdgesPerNode

◆ m_maxEdgesPerNodePerLayer

◆ m_minSegmentsPerComponent

◆ m_pairGateDecorKey

◆ m_pairGateThreshold

◆ m_recoverOrphanNodes

◆ m_requireMutualTopKEdges

◆ m_seedAnchorsPerComponent

◆ m_segmentKey

◆ m_sumBkgEdgesPassed

◆ m_sumBkgEdgesTotal

◆ m_sumChamberSuppressedNodes

◆ m_sumComponentsKept

◆ m_sumGraphNodes

◆ m_sumInputBkgSegments

◆ m_sumInputTruthSegments

◆ m_sumMutualTopKPairs

◆ m_sumNodesRejectedByMinComponent

◆ m_sumOneSidedTopKPairs

◆ m_sumOnnxBkgSegments

◆ m_sumOnnxTruthSegments

◆ m_sumOrphanRecoveryPairs

◆ m_sumRejectedComponents

◆ m_sumRetainedBkgSegments

◆ m_sumRetainedNodes

◆ m_sumRetainedTruthSegments

◆ m_sumThresholdedNodes

◆ m_sumTopologyNodes

◆ m_sumTrueEdgesPassed

◆ m_sumTrueEdgesTotal

◆ m_truthDiagEnabled

◆ m_useDegreeCappedComponents

◆ m_varHandleArraysDeclared