论文标题
鹈鹕：用于粒子物理物理的置换术和洛伦兹不变或协变量聚合网络
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics
论文作者
论文摘要
粒子物理学中的机器学习的许多当前方法都使用通用体系结构，这些架构需要大量参数并无视基本的物理原理，从而将其适用性限制为科学建模工具。在这项工作中，我们提出了一种机器学习体系结构，该体系结构使用一组相对于完整的6维Lorentz对称性，最大程度地减少的输入，并且整个过程中都是完全置换的。我们研究了该网络体系结构在顶级夸克标签的标准任务中的应用，并表明，尽管模型复杂性要低得多，但最终的网络还是优于所有现有竞争对手。此外，我们提出了应用于4 Momentum回归任务的同一网络的Lorentz-Covariant变体。
Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools. In this work, we present a machine learning architecture that uses a set of inputs maximally reduced with respect to the full 6-dimensional Lorentz symmetry, and is fully permutation-equivariant throughout. We study the application of this network architecture to the standard task of top quark tagging and show that the resulting network outperforms all existing competitors despite much lower model complexity. In addition, we present a Lorentz-covariant variant of the same network applied to a 4-momentum regression task.
