Comparing Generative Models with the New Physics Learning Machine

Samuele Grossi; Marco Letizia; Riccardo Torre

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arXiv · 2508.02275

Comparing Generative Models with the New Physics Learning Machine

Samuele Grossi·Marco Letizia·Riccardo Torre

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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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BibTeX RIS

Samuele Grossi, Marco Letizia, Riccardo Torre. 2025-08-04. Comparing Generative Models with the New Physics Learning Machine. https://arxiv.org/abs/2508.02275

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