标题： 新物理现象在异常检测中的敏感性：不可调超参数的研究 显示英文标题

标题： Sensitivity to New Physics Phenomena in Anomaly Detection: A Study of Untunable Hyperparameters

摘要： 在对撞机实验中寻找超出标准模型（SM）的物理现象需要独立于模型的策略，以避免遗漏意外信号的可能发现。异常检测（AD）技术通过识别偏离标准模型的现象提供了一种有前景的方法，并已得到广泛研究。然而，这些方法对无法调整的超参数的敏感性尚未系统地进行比较。 本研究通过调查四种半监督异常检测方法——自动编码器、深度支持向量数据描述、基于直方图的离群分数和孤立森林——在模拟的标准模型背景事件上进行训练来解决这一问题。本文研究了这些方法对无法调整的超参数变化下超出标准模型基准信号的敏感性。此类研究通过提出一种使用与信号无关统计量的非参数置换检验得以补充，这可以提供稳健的统计评估。 显示英文摘要

摘要： The search for physics beyond the Standard Model (BSM) at collider experiments requires model-independent strategies to avoid missing possible discoveries of unexpected signals. Anomaly detection (AD) techniques offer a promising approach by identifying deviations from the Standard Model (SM) and have been extensively studied. The sensitivity of these methods to untunable hyperparameters has not been systematically compared, however. This study addresses it by investigating four semi-supervised AD methods -- Auto-Encoders, Deep Support Vector Data Description, Histogram-based Outlier Score, and Isolation Forest -- trained on simulated SM background events. In this paper, we study the sensitivity of these methods to BSM benchmark signals as a function of these untunable hyperparameters. Such a study is complemented by a proposal of a non-parametric permutation test using signal-agnostic statistics, which can provide a robust statistical assessment.