Document Type

Article

Publication Date

2026

DOI

10.3934/aci.2026008

Publication Title

Applied Computing and Intelligence

Volume

6

Issue

2

Pages

138-156

Abstract

In particle and nuclear physics, “detector effects unfolding” can be viewed as a highdimensional inverse problem whose goal is to recover the true event distributions from observed experimental data corrupted by detector-induced distortions. Recent advances in generative AI have positioned data-driven and machine learning-based approaches as powerful alternatives to traditional unfolding techniques, offering superior scalability to high-dimensional data, capability of learning complex detector responses, and the ability to operate directly at the event level. We survey state-of the-art generative AI-based models for detector folding and unfolding. We review existing architectures and training strategies, and highlight recent methodological advances and open challenges. Through a detailed discussion of latent space representations, uncertainty quantification, physics insights, and background removals, we demonstrate the potential to significantly advance the field.

Rights

© 2026 The Authors.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Alghamdi, T., Vittorini, T., Xu, J., Battaglieri, M., Glazier, D. I., Montaña, G., Foti, G., Pilloni, A., Sato, N., & Li, Y. (2026). A survey on generative AI for detector effects unfolding in particle and nuclear physics. Applied Computing and Intelligence, 6(2), 138-156. https://doi.org/10.3934/aci.2026008

ORCID

0000-0002-5640-3824 (Alghamdi), 0000-0002-3575-2128 (Xu)

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