A controlled benchmark of six ML classifiers on a new simulated ttH multilepton dataset finds symmetry-constrained graph models (Particle Transformer, LorentzNet) and azimuthal RoPE encoding outperform tabular baselines.
CERN GitLab, https://gitlab.cern.ch/TRExStats/TRExFitter (2024)
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Benchmarking Machine Learning Architectures for ttH Multilepton Signal Sensitivity
A controlled benchmark of six ML classifiers on a new simulated ttH multilepton dataset finds symmetry-constrained graph models (Particle Transformer, LorentzNet) and azimuthal RoPE encoding outperform tabular baselines.