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Abstract In this study, we investigate the application of the New Physics Learning Machine (NPLM) algorithm as an alternative to the standard CWoLa method with Boosted Decision Trees (BDTs), particularly for scenarios with rare signal events. NPLM offers an end-to-end approach to anomaly detection and hypothesis testing by utilizing an in-sample evaluation of a binary classifier to estimate a log-density ratio, which can improve detection performance without prior assumptions on the signal model. We examine two approaches: (1) a end-to-end NPLM application in cases with reliable background modelling and (2) an NPLM-based classifier used for signal selection when accurate background modelling is unavailable, with subsequent performance enhancement through a hyper-test on multiple values of the selection threshold. Our findings show that NPLM-based methods outperform BDT-based approaches in detection performance, particularly in low signal injection scenarios, while significantly reducing epistemic variance due to hyperparameter choices. This work highlights the potential of NPLM for robust resonant anomaly detection in particle physics, setting a foundation for future methods that enhance sensitivity and consistency under signal variability.
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This content will become publicly available on July 7, 2027
Learning to validate generative models: a goodness-of-fit approach
Abstract Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation. Classic approaches struggle with scalability, statistical power, or interpretability when applied to high-dimensional data, making it difficult to certify the reliability of these models in realistic, high-dimensional scientific settings. Here, we propose the use of the New Physics Learning Machine (NPLM), a learning-based approach to goodness-of-fit testing inspired by the Neyman–Pearson construction, to test generative networks trained on high-dimensional scientific data. We demonstrate the performance of NPLM for validation in two benchmark cases: generative models trained on mixtures of Gaussian models with increasing dimensionality, and a public end-to-end model developed to generate high-energy physics collision events. We show that NPLM can serve as a powerful validation method while also providing a means to diagnose sub-optimally modeled regions of the data.
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- Award ID(s):
- 2019786
- PAR ID:
- 10707677
- Publisher / Repository:
- Machine Learning Science and Technology
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 7
- Issue:
- 4
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- 045011
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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