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.

异常检测技术在大型强子对撞机 (LHC) 中变得越来越重要，其动机是越来越需要以与模型无关的方式搜索新物理。在这项工作中，我们对经过充分研究的无监督方法称为自动编码器 (AE) 和基于无标签分类 (CWoLa) 技术的弱监督方法进行了详细的比较研究。我们检查了这两种方法在完全强子共振搜索中识别不同横截面的新物理信号的能力。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敏感性。特别是，CWoLa 可以有效地发现多样化和中等稀有信号，而 AE 可以提供对非常稀有信号的敏感性，但仅限于某些拓扑结构。因此，我们证明这两种技术是互补的，可以一起用于 LHC 的异常检测。