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 applicational 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优势。近年来，基于能量的模型在计算机视觉与自然语言处理的高维数据建模领域取得了显著成功。受这些进展启发，我们构建了一种面向大型强子对撞机高能物理事件的多用途基于能量概率模型。该框架以强大的生成模型为基础，能够描述高阶粒子间相互作用，兼容不同编码架构，并基于隐式生成机制运行。在应用层面，该模型可充当用于物理仿真的强大参数化事件生成器、不受虚假相关性干扰的通用反常信号检测器，以及用于粒子识别的增强型事件分类器。