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Bibliographic Details
Main Authors:	Diefenbacher, Sascha, Schweitzer, Sofia Palacios, Kasieczka, Gregor
Format:	Preprint
Published:	2026
Subjects:	High Energy Physics - Phenomenology Machine Learning Data Analysis, Statistics and Probability
Online Access:	https://arxiv.org/abs/2605.30453
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Table of Contents:

Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power.