基于似然的CMS双喷注事件无监督异常检测

中文摘要 AI 辅助

我们提出了一种利用神经样条流密度估计对质子-质子对撞数据中的异常双喷注事件进行无监督搜索的方法。归一化流模型在高维特征空间上训练，该特征空间包含喷注、双喷注和事件级观测量，直接从数据中学习主导的标准模型本底，而不假设特定的信号假设。在学习到的密度下具有低似然的事件被识别为潜在异常事件。使用该方法对CMS开放数据双喷注样本进行研究，我们调查了异常分数分布尾部的极端事件，并进行了广泛的验证和稳健性研究。该研究包括特征级统计比较、质量去关联测试、基于置换的零假设测试以及训练稳定性评估。选定的异常事件表现出与仅本底预期显著偏离，主要在喷注子结构观测量中，同时在无监督学习的几个已知偏差来源下保持稳定。识别出的异常分布在运动学相空间中，并未在双喷注不变质量谱中表现出窄结构。相反，它们在多个观测量上显示出相关偏差，与喷注子结构和事件拓扑的多变量差异一致，而非局部共振。虽然未声称发现新物理，但这项研究表明，基于神经样条流的密度估计可以对对撞机数据中罕见的、结构化的偏差敏感，并可能为LHC上超越标准模型物理的搜索提供模型无关的探索工具。

英文摘要

We present an unsupervised search for anomalous dijet events in proton--proton collision data using neural spline flow density estimation. A normalizing flow model is trained on a high-dimensional feature space comprising jet, dijet, and event-level observables to learn the dominant Standard Model background directly from data, without assuming a specific signal hypothesis. Events assigned low likelihood under the learned density are identified as potential anomalous events. Using this approach on a CMS Open Data dijet sample, we investigate extreme events in the tail of the anomaly-score distribution and perform an extensive validation and robustness study. This study includes feature-level statistical comparisons, mass decorrelation tests, permutation-based null tests, and evaluations of training stability. The selected anomalous events exhibit notable departures from the background-only expectation, primarily in jet-substructure observables, while remaining stable under several known sources of bias in unsupervised learning. The identified anomalies are distributed across the kinematic phase space and do not exhibit a narrow structure in the dijet invariant-mass spectrum. Instead, they show correlated deviations across multiple observables, consistent with a multivariate difference in jet substructure and event topology rather than a localized resonance. While no claim of new physics is made, this study demonstrates that neural spline flow-based density estimation can be sensitive to rare, structured deviations in collider data and may provide a model-independent exploratory tool for searches for physics beyond the Standard Model at the LHC.

发表机构

• Mohan Babu University（莫汉·巴布大学）
• VIT-AP University（VIT-AP大学）

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