  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.
