689635 event 1776091139 1776091209 Dr. Inbar Savoray is a particle physics phenomenologist and postdoctoral fellow at the Leinweber Institute for Theoretical Physics at UC Berkeley and the Lawrence Berkeley National Laboratory. She earned her Ph.D. from the Weizmann Institute of Science in 2023, and was recognized with the national Yoel Rakah Prize for an outstanding theoretical student from the Israel Physical Society, as well as the John F. Kennedy Prize and the Physics Faculty Prize from the Weizmann Institute. This fall, Inbar will join MIT as a Leinweber Postdoctoral Fellow at the Center of Theoretical Physics - a Leinweber Institute.

]]> 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 the invariance and the performance of state-of-the-art models without changing their architecture.
 

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