A survey on generative AI for detector effects unfolding in particle and nuclear physics

Tareq Alghamdi; Tommaso Vittorini; Jitao Xu; Marco Battaglieri; Derek I. Glazier; Glòria Montaña; Giorgio Foti; Alessandro Pilloni; Nobuo Sato; Yaohang Li; Tareq Alghamdi; Tommaso Vittorini; Jitao Xu; Marco Battaglieri; Derek I. Glazier; Glòria Montaña; Giorgio Foti; Alessandro Pilloni; Nobuo Sato; Yaohang Li

doi:10.3934/aci.2026008

Applied Computing and Intelligence

2026, Volume 6, Issue 2: 138-156. doi: 10.3934/aci.2026008

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Survey

A survey on generative AI for detector effects unfolding in particle and nuclear physics

1.
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
2.
Department of Computer Science, Al-Baha University, Al-Baha, Alaqiq 65779, Saudi Arabi
3.
INFN sezione di Genova, I-16146 Genova, Italy
4.
SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, United Kingdom
5.
Thomas Jefferson National Accelerator Facility, Newport News, VA 23606, USA
6.
Department de Fısica Quantica i Astrofisica and ICCUB, Universitat de Barcelona, E-08028 Barcelona, Spain
7.
Università degli studi di Messina, I-98166 Messina, Italy
8.
INFN Sezione di Catania, I-95123 Catania, Italy
^† These authors contributed equally to this work

Received: 23 May 2026 Revised: 03 July 2026 Accepted: 10 July 2026 Published: 28 July 2026

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-ofthe-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.
- generative AI,
- detector effects,
- unfolding,
- particle physics,
- nuclear physics,
- uncertainty quantification,
- inverse problems
Citation: Tareq Alghamdi, Tommaso Vittorini, Jitao Xu, Marco Battaglieri, Derek I. Glazier, Glòria Montaña, Giorgio Foti, Alessandro Pilloni, Nobuo Sato, Yaohang Li. A survey on generative AI for detector effects unfolding in particle and nuclear physics[J]. Applied Computing and Intelligence, 2026, 6(2): 138-156. doi: 10.3934/aci.2026008

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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-ofthe-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.

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