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Encoding physics at the precision frontier

Victor Bresó-Pla1, Kevin Greif2, Vinicius Mikuni3, Benjamin Nachman4,5, Tilman Plehn6,7, Tanvi Wamorkar4, and Daniel Whiteson2

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Phys. Rev. D 114, 036034 – Published 31 August, 2026

DOI: https://doi.org/10.1103/9hs2-prs5

Abstract

High-performance machine learning tools in particle physics rest on two complementary directions: encoding symmetries explicitly in the architecture, and implicitly learning the structure of the data through large-scale (pre)training. We compare the performance of the representative Lorentz Geometric Algebra Transformer and omnilearn models on three especially challenging tasks: reweighting-based unfolding, likelihood-ratio estimation, and weakly supervised anomaly detection. Across all benchmarks, both methods achieve comparable performance given the statistical precision of the finetuning datasets, suggesting that the significant efficiency gains from encoding known particle physics structures are largely method independent.

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Phys. Rev. D 114, 036034– Published 31 August, 2026
  • Received 23 March 2026
  • Accepted 23 July 2026
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