Neural network-based deconvolution for GeV-Scale Gamma-Ray Spectroscopy
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917426381193216 |
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| author | Zhang, Zhuofan Wei, Mingxuan Fleck, Kyle Liu, Jun Tan, Xinjian Sarri, Gianluca Yan, Wenchao |
| author_facet | Zhang, Zhuofan Wei, Mingxuan Fleck, Kyle Liu, Jun Tan, Xinjian Sarri, Gianluca Yan, Wenchao |
| contents | High-energy gamma-ray spectroscopy is crucial for studying and advancing the application of high-energy photons in areas like strong-field physics, high-energy-density science, and laboratory astrophysics. However, high-energy gamma-ray spectroscopy in the multi-MeV to GeV range faces significant challenges in precise spectral reconstruction. This study presents a machine learning-based inversion approach that combines a spectrometer design with advanced deconvolution algorithms. We develop a gamma-ray spectrometer optimized through Monte Carlo simulations for maximum positron yield and minimal noise. A two-stage neural network framework is proposed based on the structure of the spectrometer: a denoising autoencoder suppresses statistical noise in measured positron spectra, while a U-Net architecture solves the ill-posed inverse problem to reconstruct incident gamma spectra. This approach establishes a new methodology for gamma-ray diagnostics in strong-field QED experiments and high-energy photon sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01612 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural network-based deconvolution for GeV-Scale Gamma-Ray Spectroscopy Zhang, Zhuofan Wei, Mingxuan Fleck, Kyle Liu, Jun Tan, Xinjian Sarri, Gianluca Yan, Wenchao Instrumentation and Detectors High-energy gamma-ray spectroscopy is crucial for studying and advancing the application of high-energy photons in areas like strong-field physics, high-energy-density science, and laboratory astrophysics. However, high-energy gamma-ray spectroscopy in the multi-MeV to GeV range faces significant challenges in precise spectral reconstruction. This study presents a machine learning-based inversion approach that combines a spectrometer design with advanced deconvolution algorithms. We develop a gamma-ray spectrometer optimized through Monte Carlo simulations for maximum positron yield and minimal noise. A two-stage neural network framework is proposed based on the structure of the spectrometer: a denoising autoencoder suppresses statistical noise in measured positron spectra, while a U-Net architecture solves the ill-posed inverse problem to reconstruct incident gamma spectra. This approach establishes a new methodology for gamma-ray diagnostics in strong-field QED experiments and high-energy photon sources. |
| title | Neural network-based deconvolution for GeV-Scale Gamma-Ray Spectroscopy |
| topic | Instrumentation and Detectors |
| url | https://arxiv.org/abs/2512.01612 |