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Accepted manuscript

Neural network-based deconvolution for GeV-Scale Gamma-Ray Spectroscopy

Published online by Cambridge University Press:  21 April 2026

Zhuofan Zhang Open the ORCID record for Zhuofan Zhang [Opens in a new window] ,
Mingxuan Wei ,
Kyle Fleck ,
Jun Liu ,
Xinjian Tan ,
Gianluca Sarri Open the ORCID record for Gianluca Sarri [Opens in a new window]  and
Wenchao Yan Open the ORCID record for Wenchao Yan [Opens in a new window]
Zhuofan Zhang
Affiliation:
State Key Laboratory of Dark Matter Physics, Key Laboratory of Laser Plasma (MoE), School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
Mingxuan Wei
Affiliation:
State Key Laboratory of Dark Matter Physics, Key Laboratory of Laser Plasma (MoE), School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
Kyle Fleck
Affiliation:
School of Mathematics and Physics, Queen’s University Belfast, BT7 1NN Belfast, United Kingdom
Jun Liu
Affiliation:
State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China liujun@nint.ac.cn
Xinjian Tan
Affiliation:
State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China
Gianluca Sarri
Affiliation:
School of Mathematics and Physics, Queen’s University Belfast, BT7 1NN Belfast, United Kingdom
Wenchao Yan*
Affiliation:
State Key Laboratory of Dark Matter Physics, Key Laboratory of Laser Plasma (MoE), School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of IFSA, Shanghai Jiao Tong University, Shanghai 200240, China
*
†wenchaoyan@sjtu.edu.cn
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Information

Type
Research Article
Information
High Power Laser Science and Engineering , Accepted manuscript , pp. 1 - 13
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press in association with Chinese Laser Press