Reconstructing Gamma-ray Energy Distributions from PEDRO Pair Spectrometer Data

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...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Yadav, M, Oruganti, M H, Naranjo, B, Andonian, G, Apsimon, Ö, Welsch, C P, Rosenzweig, J B
Format: Article
Language:English
Subjects:
Beam interactions
Decomposition
Electron beams
Electron-positron pairs
Electrons
Energy
Energy distribution
Energy spectra
Gamma rays
High energy electrons
Machine learning
Maximum likelihood estimation
Neural networks
Photon beams
Photons
Short pulses
Spectral emittance
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Summary: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.
ISSN:2331-8422