共振异常检测的弱监督和无监督方法比较
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
摘要：
在大型强子对撞机（LHC）中，异常检测技术的重要性越来越大，这是由于越来越需要以模型不可知的方式寻找新的物理学。在这项工作中，我们提供了一个详细的比较研究之间的研究称为自动编码器（AE）和弱监督的方法的基础上分类无标签（CWoLa）技术。我们研究的能力，这两种方法来确定一个新的物理信号在不同的截面在一个完全的强子共振搜索。通过构造，AE分类性能与注入信号的量无关。相比之下，CWoLa性能随着信号丰度的增加而提高。当将这些方法与完整的背景估计相结合时，我们发现这两种方法具有互补的灵敏度。特别是，CWoLa可以有效地发现多样化和中等罕见的信号，而AE可以对非常罕见的信号提供灵敏度，但仅限于某些拓扑结构。因此，我们表明，这两种技术是互补的，可以一起使用的异常检测在大型强子对撞机。
英文摘要：
Anomaly detection techniques are growing in importance at the Large Hadron Collider (LHC), motivated by the increasing need to search for new physics in a model-agnostic way. In this work, we provide a detailed comparative study between a well-studied unsupervised method called the autoencoder (AE) and a weakly-supervised approach based on the Classification Without Labels (CWoLa) technique. We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search. By construction, the AE classification performance is independent of the amount of injected signal. In contrast, the CWoLa performance improves with increasing signal abundance. When integrating these approaches with a complete background estimate, we find that the two methods have complementary sensitivity. In particular, CWoLa is effective at finding diverse and moderately rare signals while the AE can provide sensitivity to very rare signals, but only with certain topologies. We therefore demonstrate that both techniques are complementary and can be used together for anomaly detection at the LHC.
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