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

TL;DR

This paper introduces a neural network-based method for precise gamma-ray spectral reconstruction in high-energy physics, combining spectrometer design with advanced deconvolution algorithms to improve accuracy in GeV-scale gamma-ray spectroscopy.

Contribution

It develops a novel two-stage neural network framework tailored to gamma-ray spectrometry, enhancing spectral reconstruction accuracy over traditional methods.

Findings

The neural network approach effectively suppresses noise in measured spectra.

The method accurately reconstructs incident gamma spectra from simulated data.

It offers a new diagnostic tool for high-energy photon experiments.

Abstract

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.