https://doi.org/10.1140/epjc/s10052-024-13722-5
Regular Article - Experimental Physics
Multiple testing for signal-agnostic searches for new physics with machine learning
1
NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, MA, USA
2
MIT Laboratory for Nuclear Science, Cambridge, MA, USA
3
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
4
MaLGa-DIBRIS, University of Genoa, Genoa, Italy
5
INFN, Sezione di Genova, Genoa, Italy
Received:
25
September
2024
Accepted:
17
December
2024
Published online:
4
January
2025
In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on machine learning, where model selection can introduce a bias towards specific families of new physics signals. Focusing on the New Physics Learning Machine, a methodology to perform a signal-agnostic likelihood-ratio test, we explore a number of approaches to multiple testing, such as combining p-values and aggregating test statistics. Our findings show that it is beneficial to combine different tests, characterised by distinct choices of hyperparameters, and that performances comparable to the best available test are generally achieved, while also providing a more uniform response to various types of anomalies. This study proposes a methodology that is valid beyond machine learning approaches and could in principle be applied to a larger class model-agnostic analyses based on hypothesis testing.
© The Author(s) 2024
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Funded by SCOAP3.
