Speaker
Description
Particle physics is governed by a number of fundamental symmetries including Lorentz symmetry, gauge symmetries of the Standard Model, and discrete symmetries like charge, parity, and time. Consequently, designing equivariant ML architectures has emerged as a popular method for incorporating physics-inspired inductive biases into ML models. In this work, we evaluate commonly cited benefits of equivariant architectures for jet tagging and particle tracking, including model accuracy and generalizability and model/data efficiency. We conclude that many of the proposed benefits of equivariant models do not universally hold. We then discuss possible reasons this may be the case, including limited expressivity of equivariant models and symmetry breaking introduced by the experimental apparatuses used to collect physics data. We explore semi-equivariant architectures as a possible solution to address these limitations and introduce preliminary explainability studies that seek to characterize possible differences in what unconstrained, equivariant, and semi-equivariant architectures are learning in order to model the same physics task.
