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The photon flux resulting from high-energy electron beam interactions withhigh field systems, such as in the upcoming FACET-II experiments at SLACNational Accelerator Laboratory, may give deep insight into the electron beam'sunderlying dynamics at the interaction point. Extraction of this information isan intricate process, however. To demonstrate how to approach this challengewith modern methods, this paper utilizes data from simulated plasma wakefieldacceleration-derived betatron radiation experiments and high-fieldlaser-electron-based radiation production to determine reliable methods ofreconstructing key beam and interaction properties. For these measurements,recovering the emitted 200 keV to 10 GeV photon energy spectra from twoadvanced spectrometers now being commissioned requires testing multiple methodsto finalize a pipeline from their responses to incident electron beaminformation. In each case, we compare the performance of: neural networks,which detect patterns between data sets through repeated training; maximumlikelihood estimation (MLE), a statistical technique used to determine unknownparameters from the distribution of observed data; and a hybrid approachcombining the two. Further, in the case of photons with energies above 30 MeV,we also examine the efficacy of QR decomposition, a matrix decompositionmethod. The betatron radiation and the high-energy photon cases demonstrate theeffectiveness of a hybrid ML-MLE approach, while the high-field electrodynamicsinteraction and the low-energy photon cases showcased the machine learning (ML)model's efficiency in the presence of noise. As such, while there is utility inall the methods, the ML-MLE hybrid approach proves to be the mostgeneralizable.

Machine learning-based analysis of experimental electron beams and gamma energy distributions