Multiple testing for signal-agnostic searches for new physics with machine learning
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. Fo...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-024-13722-5 |
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| _version_ | 1841559124589936640 |
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| author | Gaia Grosso Marco Letizia |
| author_facet | Gaia Grosso Marco Letizia |
| author_sort | Gaia Grosso |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2782376cee774ffc854917928db9fc0e |
| institution | Kabale University |
| issn | 1434-6052 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj-art-2782376cee774ffc854917928db9fc0e2025-01-05T12:43:59ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522025-01-0185111310.1140/epjc/s10052-024-13722-5Multiple testing for signal-agnostic searches for new physics with machine learningGaia Grosso0Marco Letizia1NSF AI Institute for Artificial Intelligence and Fundamental InteractionsMaLGa-DIBRIS, University of GenoaAbstract 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.https://doi.org/10.1140/epjc/s10052-024-13722-5 |
| spellingShingle | Gaia Grosso Marco Letizia Multiple testing for signal-agnostic searches for new physics with machine learning European Physical Journal C: Particles and Fields |
| title | Multiple testing for signal-agnostic searches for new physics with machine learning |
| title_full | Multiple testing for signal-agnostic searches for new physics with machine learning |
| title_fullStr | Multiple testing for signal-agnostic searches for new physics with machine learning |
| title_full_unstemmed | Multiple testing for signal-agnostic searches for new physics with machine learning |
| title_short | Multiple testing for signal-agnostic searches for new physics with machine learning |
| title_sort | multiple testing for signal agnostic searches for new physics with machine learning |
| url | https://doi.org/10.1140/epjc/s10052-024-13722-5 |
| work_keys_str_mv | AT gaiagrosso multipletestingforsignalagnosticsearchesfornewphysicswithmachinelearning AT marcoletizia multipletestingforsignalagnosticsearchesfornewphysicswithmachinelearning |