arXiv 2103.091032021-03-16hep-phhep-ex

夸克/胶子喷注分类的安全性

Safety of Quark/Gluon Jet Classification

Alexis Romero, Daniel Whiteson, Michael Fenton, Julian Collado, Pierre Baldi

摘要：

在分析高能粒子碰撞的数据和寻找标准模型之外的物理过程中，将喷流分类为夸克与胶子引发的喷流是一项重要而又具有挑战性的任务。最近，在低水平探测器信息上运行的深度神经网络的集成，使夸克/胶子喷注标记模型的分类能力得到了显着提高。然而，在模拟样本上训练的这种模型的能力提高是以降低可解释性为代价的，这引起了人们对其可靠性的担忧。我们阐明夸克/胶子喷流分类决策背后的物理学，通过比较网络的性能，没有红外线和共线安全的约束，并通过揭示学习模型的能量和角度依赖性来识别不安全信息的性质。这反过来又使我们能够近似的低级别网络的性能（99%或更高）使用等效的可解释的高级别的可观的，它可以用来探测模拟样本的保真度和定义系统的不确定性。

英文摘要：

The classification of jets as quark- versus gluon-initiated is an important yet challenging task in the analysis of data from high-energy particle collisions and in the search for physics beyond the Standard Model. The recent integration of deep neural networks operating on low-level detector information has resulted in significant improvements in the classification power of quark/gluon jet tagging models. However, the improved power of such models trained on simulated samples has come at the cost of reduced interpretability, raising concerns about their reliability. We elucidate the physics behind quark/gluon jet classification decisions by comparing the performance of networks with and without constraints of infrared and collinear safety, and identify the nature of the unsafe information by revealing the energy and angular dependence of the learned models. This in turn allows us to approximate the performance of the low-level networks (by 99\% or higher) using equivalent sets of interpretable high-level observables, which can be used to probe the fidelity of the simulated samples and define systematic uncertainties.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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