发表机构
Jožef Stefan Institute; The Institute of Mathematical Sciences; Indian Institute of Science Education and Research Kolkata（约瑟夫·斯特凡研究所; 数学科学研究所; 印度教育研究科学院加尔各答分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于喷注角度和能量关联函数的广义动态半径喷注聚类算法，在高堆积和探测器效应下，相比固定半径anti-k_t，能改进增强重粒子喷注重建并提升信号-背景区分能力。
AI 中文摘要
喷注及其重建在强子对撞机的精确测量和新物理搜寻中发挥着核心作用。传统的喷注聚类算法采用固定的半径参数，这可能无法最优地描述包含不同特征尺寸喷注的事件。基于最近提出的动态半径喷注聚类框架，我们构建了几种基于喷注角度和能量关联函数的子结构启发的动态半径方案。对这些算法进行了详细研究，包括在Delphes框架内的探测器效应以及对应于每个事件平均150次相互作用的高堆积（pileup）存在下的情况，堆积污染使用PUPPI算法进行缓解。将所提出算法的性能与标准anti-$k_t$算法在增强的$Vj$（$V=W^\pm,Z$）和$tj$事件中相对于双喷注背景进行比较。利用喷注子结构观测量和基于提升决策树的多变量分析，我们发现动态半径算法相比传统的固定半径anti-$k_t$聚类，能够改进增强重粒子喷注的重建，并实现更好的信号-背景区分。
英文摘要
Jets and their reconstructions play a central role in precision measurements and searches for new physics at hadron colliders. Conventional jet clustering algorithms employ a fixed radius parameter, which may not optimally describe events containing jets of varying characteristic sizes. Building on the recently proposed dynamic radius jet clustering framework, we construct several substructure-inspired dynamic radius prescriptions based on jet angularities and energy correlation functions. A detailed study of these algorithms is performed, including detector effects within the Delphes framework and in the presence of high pileup corresponding to an average of 150 interactions per event, with pileup contamination mitigated using the PUPPI algorithm. The performance of the proposed algorithms is compared with that of the standard anti-$k_t$ algorithm in boosted $Vj$ ($V=W^\pm,Z$) and $tj$ events against dijet backgrounds. Using jet substructure observables and multivariate analysis based on boosted decision trees, we find that the dynamic radius algorithms lead to improved reconstruction of boosted heavy-particle jets and achieve better signal-background discrimination compared to the conventional fixed radius anti-$k_t$ clustering.
Comments33 pages, 10 figures, and 4 tables
