A key question for machine learning approaches in particle physics is how to
best represent and learn from collider events. As an event is intrinsically a
variable-length unordered set of particles, we build upon recent machine
learning efforts to learn directly from sets of features or "point clouds".
Adapting and specializing the "Deep Sets" framework to particle physics, we
introduce Energy Flow Networks, which respect infrared and collinear safety by
construction. We also develop Particle Flow Networks, which allow for general
energy dependence and the inclusion of additional particle-level information
such as charge and flavor. These networks feature a per-particle internal
(latent) representation, and summing over all particles yields an overall
event-level latent representation. We show how this latent space decomposition
unifies existing event representations based on detector images and radiation
moments. To demonstrate the power and simplicity of this set-based approach, we
apply these networks to the collider task of discriminating quark jets from
gluon jets, finding similar or improved performance compared to existing
methods. We also show how the learned event representation can be directly
visualized, providing insight into the inner workings of the model. These
architectures lend themselves to efficiently processing and analyzing events
for a wide variety of tasks at the Large Hadron Collider. Implementations and
examples of our architectures are available online in our EnergyFlow package.

此篇文章介绍了如何利用机器学习技术处理粒子物理中的碰撞事件数据，并提出了一种基于深度集合（Deep Sets）框架的粒子流网络（Energy Flow Networks）和粒子流网络（Particle Flow Networks）方法，这些方法能有效地处理碰撞事件数据，对于区分夸克喷流和胶子喷流等任务性能表现出与现有方法相似或更好的结果，并提供了可视化方式解释模型内部的工作。