https://doi.org/10.1140/epjp/s13360-025-06679-1
Regular Article
A new generative framework for fast parametric modeling of 
and 
ray energy spectra using VAE
1
Jülich Centre for Neutron Science, Forschungszentrum Jülich GmbH, 52428, Jülich, Germany
2
Computer Science Department, Univ Evry-Paris-Saclay, 40 rue du Pelvoux, 91020, Evry Courcouronnes, France
a
i.meleshenkovskii@fz-juelich.de
Received:
13
May
2025
Accepted:
19
July
2025
Published online:
5
September
2025
This paper proposes a variational autoencoder-based technique for the fast modeling of various or ray spectra, including the parametrization of the physical measurement conditions, and introduces the SpectroGAN simulation tool. The generated spectra reproduce the training data with high precision. Still, they may vary according to the parameters of interest, such as detector energy resolution, efficiency, attenuation, activity, enrichment, or isotopic composition. Our results demonstrate that machine learning-based spectra simulations have several potential advantages over Monte Carlo-based simulation methods. First, they are much faster and more efficient to calculate. Second, they can provide accurate spectra even if the expected conditions are not included in the training dataset. The data-based model can learn complex relationships between the detector characteristics, measurement configuration, and the spectrometric features examined (such as signature peaks of elements). Finally, they can generate large amounts of synthetic data for training other machine learning models, such as those used in data analysis or pattern recognition. Overall, the data-based spectrometry can be generalized to various other applied radiation measurement tasks, including passive and active non-destructive measurement techniques featuring prompt and delayed neutrons and or gamma-rays detection, PGNAA and PGAINS applications, offering new opportunities for measurement system configuration optimization, as well as detector design. Official implementation is available at https://github.com/Tarysa/fast-parametric-modeling-of-X-and-ray-energy-spectra-using-VAE.
© The Author(s) 2025
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
