Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories?

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.

Automated summary

In recent years, model-independent search strategies have become increasingly popular due to the lack of a clear signal in new physics and a parameter-free extension of the standard model. This study proposes a new approach by combining multiple theoretical signal models to improve the search strategy for unknown signals.