Particle Physics Seminar: Giving Machine Learning a Boost Towards Respecting (Approximate) Symmetries

Abstract:

Machine learning (ML) has become a powerful tool for analyzing large and complex datasets. Modern ML is primarily data-driven, with scale often outperforming specially engineered architectures. In the physical sciences, however, theoretical principles such as symmetries can provide inductive biases that improve the robustness and data efficiency of ML models. While symmetries are ubiquitous in particle physics, fully symmetric models can be difficult to train and implement. Moreover, real-world experiments often exhibit broken symmetries due to imperfections and finite detector resolution. We introduce a method for building symmetry-aware ML models through soft constraints. We investigate two complementary approaches: one that encourages invariance to sampled group transformations, and one that encourages invariance to infinitesimal symmetry actions. We apply these ideas to Lorentz invariance, and find that incorporating soft constraints can improve performance while requiring negligible changes to current state-of-the-art models.

Seminar Organizer: Dr. Michael Geller