基于Kolmogorov-Arnold网络的Pythia与Herwig差异的功能解剖
Functional anatomy of Pythia-Herwig differences with Kolmogorov-Arnold networks
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中文总结 AI 辅助
本研究提出基于Kolmogorov-Arnold网络（KAN）的分阶段功能分析框架，分解Pythia与Herwig高能事件发生器的差异，明确其在不同阶段的驱动结构及传递特性，揭示重权重函数内部的隐藏信息。
中文摘要 AI 辅助
高能事件发生器之间的差异可能出现在碰撞模拟的多个阶段，从硬散射过程、部分子簇射、强子化到最终事例。这些差异通常通过可观测分布或全局分类器得分来总结，尽管这些量能量化分歧，但无法揭示可观测层面的哪些结构承载了分歧，也无法揭示这些结构是否在事例产生的不同阶段持续存在。在本研究中，我们将该问题表述为发生器模型差异的分阶段功能分析：通过仅簇射、强子化及全发生器三个层级追踪Pythia与Herwig产生的相同硬双喷注事例，我们使用分类器导出的对数密度比的可加Kolmogorov-Arnold网络（KAN）表示，将学习到的分歧分解为明确的一维可观测响应，这些响应可被分离、重组并在发生器阶段间传递。在相同的8个可观测喷注表示中，Pythia与Herwig的差异在簇射阶段主要由多重性驱动，强子化后转向喷注质量与形状，全发生器配置下则形成混合形状-多重性驱动的结构。将单个簇射阶段功能组件向下游传递的结果显示，簇射阶段的多重性信息可保留其重权重能力，而对应的形状响应则未必如此（尽管形状在后续阶段再次变得重要）；同时，喷注质量因子受限于统计支持不足。因此，该基于KAN的框架为发生器模型依赖性提供了功能解剖，揭示了单一全局分类器导出的重权重函数内部隐藏的持久结构与支持失效问题。
英文摘要
Differences between high-energy event generators can arise at several stages of the collision simulation, from the hard scattering through parton showering and hadronization to the final event. These differences are usually summarized using observable distributions or global classifier scores. While these quantify the disagreement, they do not reveal which observable-level structures carry it or whether those structures persist through different stages of event generation. In this work, we formulate this problem as a staged functional analysis of generator-model differences. Following the same hard dijet events through Pythia and Herwig at shower-only, hadronized, and full-generator levels, we use an additive Kolmogorov-Arnold network (KAN) representation of the classifier-derived log density ratio to decompose the learned discrepancy into explicit one-dimensional observable responses that can be isolated, recomposed, and transported between generator stages. Within the same eight-observable jet representation, the Pythia-Herwig difference is driven mainly by multiplicity at shower level, shifts toward jet mass and shape after hadronization, and develops a mixed shape-multiplicity driven structure in the full-generator configuration. Transporting the individual shower-level functional components downstream shows that shower-level multiplicity information can retain its reweighting power, whereas the corresponding shape responses need not do so even though shape becomes important again at later stages. The jet-mass factors, meanwhile, are limited by poor statistical support. This KAN-based framework therefore provides a functional anatomy of generator-model dependence, exposing both persistent structures and support failures that are hidden inside a single global classifier-derived reweighting function.
发表机构
- University of Puerto Rico at Mayagüez（波多黎各大学马亚圭斯分校）
机构由 AI 辅助整理，请以论文原文为准。
