The Interplay of Machine Learning--based Resonant Anomaly Detection Methods

TL;DR

This paper evaluates the complementarity of various machine learning-based resonant anomaly detection methods in high-energy physics, demonstrating that combining multiple approaches can significantly improve the detection of signals beyond the Standard Model.

Contribution

It provides a systematic analysis of the correlation and complementarity of different anomaly detection methods, highlighting the benefits of combining approaches for better BSM searches.

Findings

Different methods often identify different events as signal-like.

Combining multiple methods reduces false positives.

Method combination enhances detection sensitivity.

Abstract

Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM signal that make use of simulated or detected data in different ways, there has not yet been a study of the methods' complementarity. To this end, we address two questions. First, in the absence of any signal, do different methods pick the same events as signal-like? If not, then we can significantly reduce the false-positive rate by comparing different methods on the same dataset. Second, if there is a signal, are different methods fully correlated? Even if their maximum performance is the same, since we do not know how much signal is present, it may be beneficial to combine approaches. Using the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative answers to these questions. We find that there are significant gains possible by combining multiple methods, which will strengthen the search program at the LHC and beyond.