Multiple testing for signal-agnostic searches for new physics with machine learning
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10052_2024_Article_13722.pdf
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Author(s)
Grosso, Gaia
Letizia, Marco
Date Issued
January 4, 2025
Journal
The European Physical Journal C
Publisher
Springer Berlin Heidelberg
Citation
Grosso, G., Letizia, M. Multiple testing for signal-agnostic searches for new physics with machine learning. Eur. Phys. J. C 85, 4 (2025).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1140/epjc/s10052-024-13722-5
