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Versatile Energy-Based Probabilistic Models for High Energy Physics (2302.00695v5)

Published 1 Feb 2023 in cs.LG, hep-ex, hep-ph, and stat.ML

Abstract: As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy function. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In line with these advancements, we build a multi-purpose energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicative aspects, it can serve as a powerful parameterized event generator for physics simulation, a generic anomalous signal detector free from spurious correlations, and an augmented event classifier for particle identification.

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  1. GitHub - taolicheng/EBM-HEP: Enegy-based probabilistic modeling of high-energy events (1 star)