概率性、结构内禀的层次嵌入聚类（PSICHE）在时空中的应用：粒子物理喷注重建
Probabilistic, Structure-Intrinsic Clustering with an Hierarchical Embedding (PSICHE) in Time and Space Applied In Particle Physics Jet Reconstruction
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- University of Kansas（堪萨斯大学）
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中文总结 AI 辅助
本文提出PSICHE算法，用于粒子物理喷注重建，通过动态喷注大小、无监督多尺度特征学习、时空聚类及不确定性纳入，在自洽概率框架中解决现有方法的不足。
中文摘要 AI 辅助
喷注重建是粒子物理研究中一个活跃且开放的领域，涉及喷注大小、多重数、子结构以及在存在堆积和噪声情况下的实验性能等问题。类似的挑战在无监督学习的聚类和关联应用中普遍存在。本文介绍了一种用于喷注重建聚类的新算法：概率性、结构内禀的层次嵌入聚类（PSICHE）。PSICHE通过新颖特性解决了现有喷注重建方法的多项挑战和不足，包括但不限于：（i）动态学习且可变的喷注（簇）大小，（ii）无监督的多尺度学习涌现特征，如喷注子结构和多重数，（iii）在空间和时间上的聚类，以及（iv）纳入特定领域的实验不确定性。所有这些特性都在一个自洽的、概率性的、计算上可行的框架中实现。此外，本文针对LHC场景中各种物理现象、聚类输入、堆积条件和探测器性能参数，展示了一系列喷注重建示例中这些特性的演示。
英文摘要
Jet reconstruction is an active and open area of particle physics research, with questions related to jet size, multiplicity, substructure, and experimental performance in the presence of pileup and noise. Analogous challenges are ubiquitous throughout other applications of unsupervised learning in the context of clustering and association. This paper introduces a new algorithm for clustering in the context of jet reconstruction: Probabilistic, Structure-Intrinsic Clustering with an Hierarchical Embedding (PSICHE). PSICHE addresses several challenges and shortcomings of existing jet clustering methods with novel features, including but not limited to: (i) dynamically-learned and variable jet (cluster) sizes, (ii) unsupervised, multi-scale learning of emergent features, like jet substructure and multiplicity, (iii) clustering in space and time, and (iv) incorporation of domain-specific experimental uncertainties. All of these properties are achieved in a self-consistent, probabilistic framework that is computationally tractable. Furthermore, demonstrations of these features are presented for a range of jet clustering examples in LHC scenarios for various physics phenomena, clustering inputs, pileup conditions, and detector performance parameters.
