Reconstructing Gamma-ray Energy Distributions from PEDRO Pair Spectrometer Data

TL;DR

This paper evaluates methods for reconstructing high-energy photon spectra from pair spectrometer data, comparing traditional matrix techniques and machine learning approaches to optimize analysis of electron beam interactions.

Contribution

It introduces and compares multiple reconstruction methods, including QR decomposition and neural networks with MLE, for analyzing photon energy distributions from pair spectrometer data.

Findings

QR decomposition is theoretically most effective.

ML combined with MLE outperforms others with noisy data.

The study guides data analysis pipeline development for high-energy photon measurements.

Abstract

Photons emitted from high-energy electron beam interactions with high-field systems, such as the upcoming FACET-II experiments at SLAC National Accelerator Laboratory, may provide deep insight into the electron beam's underlying dynamics at the interaction point. With high-energy photons being utilized to generate electron-positron pairs in a novel spectrometer, there remains a key problem of interpreting the spectrometer's raw data to determine the energy distribution of the incoming photons. This paper uses data from simulations of the primary radiation emitted from electron interactions with a high-field, short-pulse laser to determine optimally reliable methods of reconstructing the measured photon energy distributions. For these measurements, recovering the emitted 10 MeV to 10 GeV photon energy spectra from the pair spectrometer currently being commissioned requires testing multiple methods to finalize a pipeline from the spectrometer data to incident photon and, by extension, electron beam information. In this study, we compare the performance QR decomposition, a matrix deconstruction technique and neural network with and without maximum likelihood estimation (MLE). Although QR decomposition proved to be the most effective theoretically, combining machine learning and MLE proved to be superior in the presence of noise, indicating its promise for analysis pipelines involving high-energy photons.