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arXiv 2108.13451hep-ph
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$e^{+}e^{-}$碰撞中辐射返回的高维异常探测

High-dimensional Anomaly Detection with Radiative Return in $e^{+}e^{-}$ Collisions

Julia Gonski, Jerry Lai, Benjamin Nachman, Inês Ochoa

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24 pages, 13 figures
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摘要：

在未来的$e^{+}e^{-}$对撞机上进行的实验将能够通过分析与初始态辐射（辐射返回）的碰撞来寻找质量低于标称质心能量的新粒子。我们表明，使用不完美或缺失的训练标签的机器学习方法可以实现对辐射返回事件中一般新粒子产生的敏感性。除了在$e^{+}e^{-}$碰撞中提出无标签分类（CWoLa）搜索方法的应用外，我们的研究还通过部署深度集神经网络架构将弱监督与变维信息相结合。我们还调查了一些辐射返回事件的异常检测的实验方面，并讨论这些在未来的探测器设计的背景下。

英文摘要：

Experiments at a future $e^{+}e^{-}$ collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods that use imperfect or missing training labels can achieve sensitivity to generic new particle production in radiative return events. In addition to presenting an application of the classification without labels (CWoLa) search method in $e^{+}e^{-}$ collisions, our study combines weak supervision with variable-dimensional information by deploying a deep sets neural network architecture. We have also investigated some of the experimental aspects of anomaly detection in radiative return events and discuss these in the context of future detector design.

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