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 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
builds on implicit generation. As for applicational aspects, it can serve as a
powerful parameterized event generator, a generic anomalous signal detector,
and an augmented event classifier.