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.

翻译：作为一种经典的生成建模方法，基于能量的模型在能量函数形式上具有天然灵活性。近年来，此类模型在计算机视觉和自然语言处理的高维数据建模中取得了巨大成功。基于这些进展，我们为大强子对撞机的高能物理事件构建了一种多功能基于能量的概率模型。该框架构建于强大的生成模型之上，能够描述高阶粒子间相互作用，适用于不同的编码架构，并基于隐式生成方法。在应用方面，它可作为物理模拟中的强参数化事件生成器、能避免虚假相关性的通用异常信号检测器，以及用于粒子识别的增强型事件分类器。