Comparing Generative Models with the New Physics Learning Machine
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915425420312576 |
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| author | Grossi, Samuele Letizia, Marco Torre, Riccardo |
| author_facet | Grossi, Samuele Letizia, Marco Torre, Riccardo |
| contents | The rise of generative models for scientific research calls for the development of new methods to evaluate their fidelity. A natural framework for addressing this problem is two-sample hypothesis testing, namely the task of determining whether two data sets are drawn from the same distribution. In large-scale and high-dimensional regimes, machine learning offers a set of tools to push beyond the limitations of standard statistical techniques. In this work, we put this claim to the test by comparing a recent proposal from the high-energy physics literature, the New Physics Learning Machine, to perform a classification-based two-sample test against a number of alternative approaches, following the framework presented in Grossi et al. (2025). We highlight the efficiency tradeoffs of the method and the computational costs that come from adopting learning-based approaches. Finally, we discuss the advantages of the different methods for different use cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparing Generative Models with the New Physics Learning Machine Grossi, Samuele Letizia, Marco Torre, Riccardo Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology The rise of generative models for scientific research calls for the development of new methods to evaluate their fidelity. A natural framework for addressing this problem is two-sample hypothesis testing, namely the task of determining whether two data sets are drawn from the same distribution. In large-scale and high-dimensional regimes, machine learning offers a set of tools to push beyond the limitations of standard statistical techniques. In this work, we put this claim to the test by comparing a recent proposal from the high-energy physics literature, the New Physics Learning Machine, to perform a classification-based two-sample test against a number of alternative approaches, following the framework presented in Grossi et al. (2025). We highlight the efficiency tradeoffs of the method and the computational costs that come from adopting learning-based approaches. Finally, we discuss the advantages of the different methods for different use cases. |
| title | Comparing Generative Models with the New Physics Learning Machine |
| topic | Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2508.02275 |