Comparing Generative Models with the New Physics Learning Machine

• URL: http://arxiv.org/abs/2508.02275v1
• Date: Mon, 04 Aug 2025 10:42:52 GMT
• Title: Comparing Generative Models with the New Physics Learning Machine
• Authors: Samuele Grossi, Marco Letizia, Riccardo Torre,
• Abstract summary: In large-scale and high-dimensional regimes, machine learning offers a set of tools to push beyond the limitations of standard statistical techniques.<n>We put this claim to the test by comparing a proposal from the high-energy physics literature, the New Physics Learning Machine, to perform a classification-based two-sample test.<n>We highlight the efficiency tradeoffs of the method and the computational costs that come from adopting learning-based approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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