Speaker
Description
Generative models are becoming an integral component of scientific workflows, yet their reliable deployment requires a rigorous understanding of their limitations through statistically robust validation. In particular, high-dimensional settings demand powerful goodness-of-fit tests that provide well-defined statistical interpretations and enable hypothesis testing. In this contribution, we propose the use of the New Physics Learning Machine (NPLM), a learning-based goodness-of-fit test inspired by the Neyman–Pearson lemma, to validate generative models trained on high-dimensional scientific data. We demonstrate its performance on two benchmark datasets: normalizing flows trained on Gaussian mixture models of increasing dimensionality, and a publicly available generative model for high-energy physics jet observables. These results demonstrate that learning-based goodness-of-fit tests provide a powerful and statistically rigorous framework for validating generative models in high-dimensional scientific applications.