Versatile Energy-Based Models for High Energy Physics

Taoli Cheng, Aaron Courville

Keywords: Generative modeling, Energy-based models, Out-of-distribution detection

Abstract: Energy-Based Models (EBMs) have the natural advantage of flexibility in the form of the energy function. Recently, EBMs have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In accordance with these signs of progress, we build a versatile energy-based 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 decomposes clearly. As for applicational aspects, it can serve as a powerful parameterized event generator, a generic anomalous signal detector, and an augmented event classifier.

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