Belle II像素检测器数据中有监督和无监督异常检测的比较
Comparison of Supervised and Unsupervised Anomaly Detection in Belle II Pixel Detector Data
摘要：
机器学习已经成为粒子对撞机实验中识别暗物质候选者的流行工具。它们能够处理大型数据集，因此适合直接对来自探测器的原始数据进行操作，而不是重建对象。在这里，我们研究了Belle II像素检测器记录的原始像素命中模式，该检测器自2019年以来一直在运行，目前具有400万像素和高达5 kHz的触发速率。特别是，我们专注于无监督技术，无需理论模型。这些与模型无关的方法允许对数据进行无偏见的探索，同时过滤掉可能暗示新物理场景的异常检测器签名。我们提出了识别假设的磁单极子对Belle II光束背景下使用自组织Kohonen地图和自动编码器。这两个无监督的算法相比，卷积多层感知器和优越的信号效率被发现在高背景抑制水平。我们的研究结果加强了使用无监督机器学习技术来补充粒子对撞机的传统搜索策略的情况，并为在不久的将来该算法的潜在在线应用铺平了道路。
英文摘要：
Machine learning has become a popular instrument for the identification of dark matter candidates at particle collider experiments. They enable the processing of large datasets and are therefore suitable to operate directly on raw data coming from the detector, instead of reconstructed objects. Here, we investigate patterns of raw pixel hits recorded by the Belle II pixel detector, that is operational since 2019 and presently features 4 M pixels and trigger rates up to 5 kHz. In particular, we focus on unsupervised techniques that operate without the need for a theoretical model. These model-agnostic approaches allow for an unbiased exploration of data, while filtering out anomalous detector signatures that could hint at new physics scenarios. We present the identification of hypothetical magnetic monopoles against Belle II beam background using Self-Organizing Kohonen Maps and Autoencoders. The two unsupervised algorithms are compared to a convolutional Multilayer Perceptron and a superior signal efficiency is found at high background rejection levels. Our results strengthen the case for using unsupervised machine learning techniques to complement traditional search strategies at particle colliders and pave the way to potential online applications of the algorithms in the near future.
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