Mixture-of-theories Training: Can We Find New Physics and Anomalies Better by Mixing Physical Theories?

TL;DR

This paper proposes a novel 'mixture-of-theories' training approach that combines multiple BSM theories to improve the detection of unknown signals in particle physics data, outperforming traditional single-model methods.

Contribution

It introduces a new mixture-of-theories training method that enhances the search for unknown physics signals by leveraging multiple theoretical models simultaneously.

Findings

Mixture-of-theories training outperforms single-model strategies.

The approach improves anomaly detection in particle physics.

It is effective in defining signal regions for new physics searches.

Abstract

Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them "in combination"? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that "mixture-of-theories training" outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.