Data for the CMSSW RecoHGCal/TICL package

Quicklinks

• TICL:
  • Reconstruction example
• TensorFlow:
  • C++ docs
  • CMSSW interface
  • PhysicsTools/TensorFlow

Models

• tf_models/energy_id_v*.pb: TensorFlow model for trackster energy regression and particle ID.

  • 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.

• tf_models/energy_regression_without_pattern_recognition.pb: TensorFlow model for trackster energy regression

  • DEPRECATED
  • This model tries to learn correction coefficients for different parts of the detector (e.g. CEE-120µm, CEH-Fine-300 µm, CEH-Coarse-Scintillators, etc.)
  • Each coefficient is implemented as a very small dense neural network with the energy sum of its category and the position (η) as input.
  • This network has been trained on layerClusters without pattern recognition. It is therefore expected that it wil perform poorly (response < 1) in any scenario after any pattern recognition has been applied. Better solutions are work in progress, so consider this as a temporary place holder to implement the functionality.

• tf_models/energy_regression_after_pattern_recognition.pb: TensorFlow model for trackster energy regression

  • for functionality: see above
  • this model is trained on hadronic single particle events (Klong)
  • Input: sum of all energies of different detector parts in an event after pattern recognition
  • Ouput: Regressed energy assuming all energy deposits originate from a single particle
  • IMPORTANT: This is not trained on individual tracksters (Work in progress) and since it is by design not linear in the energy input it will give different (i.e. worse) results if a particle is split into multiple tracksters.

• energy_regression_tracksters.pb: Tensorflow model trained on tracksters

  • Big difference: This model is trained to predict the energy correctly on trackster level, not on event level
  • For inference there is no fundamental change
  • The expected input variable are now $f_0, ..., f9, \eta, E_{raw}, p_0, ..., p1$ where $f_i = E_i / E_{raw} and $p_i$ are the ID probabilities