Versatile Energy-Based Probabilistic Models for High Energy Physics
This work provides a flexible modeling framework for particle physics simulations and analysis, though it appears incremental as an adaptation of existing energy-based models to a new domain.
The researchers developed a versatile energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider to capture higher-order inter-particle interactions, which can serve as a parameterized event generator, anomalous signal detector, and augmented event classifier.
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.