Work

Neural network-based deconvolution for GeV-scale gamma-ray spectroscopy

Year: 2026

Type: article

Abstract: High-energy gamma-ray spectroscopy is crucial for studying and advancing the application of high-energy photons in areas such as 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 method for gamma-ray diagnostics in strong-field quantum electrodynamics experiments and high-energy photon sources.

Source: High Power Laser Science and Engineering

DOI: 10.1017/hpl.2026.10142