Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918531375824896 |
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| author | Amram, Oz Letizia, Marco Kuusela, Mikael |
| author_facet | Amram, Oz Letizia, Marco Kuusela, Mikael |
| contents | Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals. Recently, new AI-based model-agnostic search strategies, many of which have been pioneered in high-energy physics, have been proposed which provide a complementary paradigm, prioritizing broad exploration over tailored analyses. These techniques offer an opportunity to enhance the overall discovery potential of modern experiments, especially in regimes where theoretical guidance is scarce. In this document, we review the conceptual framework behind the main classes of AI-based model-agnostic strategies. We discuss the potential pitfalls of these methods, and strategies for their validation and interpretation. We aim for this document to serve as a useful reference both for practitioners and for researchers interested in learning more about these model-agnostic search strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31103 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice Amram, Oz Letizia, Marco Kuusela, Mikael Data Analysis, Statistics and Probability High Energy Physics - Experiment Machine Learning Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals. Recently, new AI-based model-agnostic search strategies, many of which have been pioneered in high-energy physics, have been proposed which provide a complementary paradigm, prioritizing broad exploration over tailored analyses. These techniques offer an opportunity to enhance the overall discovery potential of modern experiments, especially in regimes where theoretical guidance is scarce. In this document, we review the conceptual framework behind the main classes of AI-based model-agnostic strategies. We discuss the potential pitfalls of these methods, and strategies for their validation and interpretation. We aim for this document to serve as a useful reference both for practitioners and for researchers interested in learning more about these model-agnostic search strategies. |
| title | Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2605.31103 |