Versatile Energy-Based Probabilistic Models for High Energy Physics

TL;DR

This paper introduces a versatile energy-based probabilistic model tailored for high energy physics, capable of generating, classifying, and detecting anomalies in collider event data with high flexibility and accuracy.

Contribution

It presents a novel multi-purpose energy-based model that captures complex particle interactions and can be used for simulation, anomaly detection, and particle classification in high energy physics.

Findings

Effective in modeling collider events with higher-order interactions

Can serve as a generative, detection, and classification tool

Demonstrates flexibility across multiple physics applications

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.