HEP-KBFI/fcc-tau

ML-Tau Model Card

This repository contains models for tau reconstruction and identification at future colliders (FCC), based on the Particle Transformer (ParT) architecture.

Dataset

• Name: 0509_dsinphi_to_sindphi
• Source: Preprocessed jet-based FCC dataset for hadronic tau reconstruction.
• Physics Processes:
  • Signal: $Z \to \tau^+\tau^-$ events.
  • Background: $Z \to q\bar{q}$ (light quarks and gluons).
• Generation & Simulation:
  • Generator: Pythia8
  • Detector Model: CLD (CLD_o2_v07) for FCC-ee.
  • Simulation: Geant4 (via ddsim).
  • Software Stack: Key4hep Project (release 2025-05-29). Key4hep-sim (v1.2.5)
  • Reconstruction: Standard CLD reconstruction (CLDReconstruction.py).
• Split: 70% Training, 10% Validation, 20% Testing.
• Input Features: 17 candidate-level features (kinematics, identification, etc.).
• Jet Composition: Maximum of 20 candidates per jet.

Dataset Statistics

• Total Jets: 9,049,163
• Signal (Tau) Jets: 1,187,870
• Background (Quark/Gluon) Jets: 7,861,293
• Training Set: 7,075,163 background + 1,069,083 signal jets
• Test Set: 786,130 background + 118,787 signal jets

Model Architecture

The models utilize the Particle Transformer (ParT) architecture, which uses a combination of particle-level and pair-level features to learn jet representations.

Variants

• MultiParTau: A multi-task learning model that simultaneously performs four tasks:
  • Tau Identification (is_tau): Binary classification (Signal tau vs. Quark/Gluon jet).
  • Charge Classification: Identification of the tau charge (+1 or -1).
  • Decay Mode Classification: 6-class classification of tau decay modes.
  • Kinematics Regression: Prediction of 5 kinematic corrections: [log(pt_gen/pt_reco), delta_eta, delta_sin(phi), delta_cos(phi), log(m_gen/m_reco)].
• SingleParTau: Specialized models trained for one of the above tasks individually.

Hyperparameters

• Embedding Dimensions: [256, 512, 256]
• Pair Embedding Dimensions: [64, 64, 64]
• Attention Heads: 8
• Transformer Layers: 2+2
• CLS Layers: 2
• Activation: GELU

Training Scheme

• Optimizer: AdamW with a weight decay of 1e-2.
• Learning Rate: 0.001 (base).
• Scheduler: OneCycleLR with cosine annealing.
• Batch Size: 12288.
• Precision: 16-mixed (FP16).
• Multi-task Strategy (MultiParTau): PCGrad (Projected Conflicting Gradients) is employed to handle gradient conflicts between different tasks during training.
• Task Weighting:
  • Tau ID: 1.0
  • Charge: 1.0
  • Decay Mode: 1.0
  • Kinematics: 2.0

Trained Models & Git Hashes

The models located in the outputs/0609_* directories correspond to the following configurations and git hashes:

Model Name	Task	Git Hash
multipartau_full	Multi-task	fef7149
single_charge	Charge	a5d7ed8
single_decaymode	Decay Mode	a5d7ed8
single_kinematics	Kinematics	a5d7ed8
single_tauid	Tau ID	a5d7ed8

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Generated based on training configurations and experiment outputs.