We present a comprehensive study of the PELICAN machine learning algorithm architecture in the context of both tagging (classification) and reconstructing (regression) Lorentz-boosted top quarks, including the difficult task of specifically identifying and measuring the $W$-boson inside the dense environment of the boosted hadronic final state. PELICAN is a novel permutation equivariant and Lorentz invariant or covariant aggregator network designed to overcome common limitations found in architectures applied to particle physics problems. Compared to many approaches that use non-specialized architectures that neglect underlying physics principles and require very large numbers of parameters, PELICAN employs a fundamentally symmetry group-based architecture that demonstrates benefits in terms of reduced complexity, increased interpretability, and raw performance. When tested on the standard task of Lorentz-boosted top quark tagging, PELICAN outperforms existing competitors with much lower model complexity and high sample efficiency. On the less common and more complex task of four-momentum regression, PELICAN also outperforms hand-crafted algorithms. We discuss the implications of symmetry-restricted architectures for the wider field of machine learning for physics.

翻译：我们针对PELICAN机器学习算法架构在洛伦兹助推顶夸克的标记（分类）与重建（回归）任务中进行了系统性研究，重点关注从助推强子末态密集环境中识别并测量W玻色子的困难任务。PELICAN是一种新型置换等变与洛伦兹不变/协变聚合网络，旨在克服粒子物理应用中现有架构的常见局限性。相较于大量采用非专业化架构（忽略基本物理原理且要求极多参数）的方法，PELICAN采用了基于对称群的基础架构，在降低复杂度、提升可解释性与原始性能方面展现出优势。在洛伦兹助推顶夸克标记的标准任务测试中，PELICAN以更低的模型复杂度和更高的样本效率超越现有算法。在难度更高的四动量回归非常规任务中，PELICAN同样优于人工设计的算法。本文进一步探讨了对称性受限架构对机器学习物理领域的影响。