Data for the CMSSW RecoHGCal/TICL package

Quicklinks

• TICL:
  • Reconstruction example
• TensorFlow:
  • C++ docs
  • CMSSW interface
  • PhysicsTools/TensorFlow
• ONNX:
  • Introduction
  • CMSSW interface (still under construction)
  • PhysicsTools/ONNXRuntime

Models

• ticlv4/tf_models/energy_id_v*.pb: The TensorFlow model for trackster energy regression and particle ID has been trained on the TICLv4 data and used within TICLv4.
  • v0: Simple CNN-based approach. The neutral pion, neutral hadron, ambiguous and unknown probabilities are set to a constant value of 0. See the talk at the Reco/AT meeting for more info. Input and output tensors:
    • "input": Input tensor with dimension batch x 50 (layers) x 10 (clusters) x 3 (features).
    • "output/id_probabilities": Output tensor with dimension batch x 8 representing particle ID "probabilities" (from a softmax output). The probabiltities refer to photon, electron, muon, neutral pion, charged hadron, neutral hadron, ambiguous and unknown cases (in that order).
    • "output/regressed_energy": Output tensor with dimension batch x 1 representing the regressed energy value for the trackster.
• superclustering/: ONNX models (from PyTorch) for superclustering of electrons.
  • superclustering/supercls_v2p1.onnx: DNN, inputs features computed from pairs of tracksters (uses inputs defined in SuperclusteringDNNInputV2 in RecoHGCal/TICL/interface/SuperclusteringDNNInputs.h). Input format : batch x 17 (features). Outputs score (dimension batch) giving "probability" that the sub-leading trackster is a bremmstrahlung photon of the leading trackster. Optimal working point : 0.3.
  • superclustering/regression_v1.onnx: DNN for supercluster energy regression. Input format : batch x 8 (features). Output : batch x 1 (supercluster regressed energy). Used in RecoHGCal/TICL/plugins/EGammaSuperclusterProducer.cc.
• ticlv5/onnx_models/: The models are trained based on TICLv5 reconstruction information using a simple CNN-based approach. Two models have been trained separately: one for trackster energy regression and one for particle ID. These models are saved in ONNX format for time optimization.
• Common input tensor: Both models share the same initial input tensor, dimensions batch x 50 (layers) x 10 (clusters) x 3 (features).
  • ticlv5/patternrecognition/id_v*.onnx:
    • "input": Input tensor with dimensions batch x 50 (layers) x 10 (clusters) x 3 (features).
    • "output/pid_output": Output tensor with dimensions batch x 8 representing particle ID probabilities (from a softmax output). The probabilities refer to: photon, electron, muon, neutral pion, charged hadron(pion), neutral hadron(kaon), ambiguous, and unknown cases (in that order). The probabilities help in classifying the particle based on its type, distinguishing between hadronic and electromagnetic categories.
  • ticlv5/linking/energy_v*.onnx:
    • "input": Input tensor with dimensions batch x 50 (layers) x 10 (clusters) x 3 (features), concatenated with the output of the particle ID model ("output/pid_output").
    • "output/enreg_output": Output Tensor with dimension batch x 1 (regressed energy). This value represents the trackster energy as estimated by the model based on the training data, compared to the true and reconstructed energies of the particle.