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@inproceedings{zhuang:ipac2021-thpab273,
author = {Y. Zhuang and B. Naranjo and J.B. Rosenzweig and M. Yadav},
title = {{Spectral Reconstruction for FACET-II Compton Spectrometer}},
booktitle = {Proc. IPAC'21},
pages = {4346--4348},
eid = {THPAB273},
language = {english},
keywords = {photon, network, electron, site, positron},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-THPAB273},
url = {https://jacow.org/ipac2021/papers/thpab273.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-THPAB273},
abstract = {{The Compton spectrometer under development at UCLA for FACET-II is a versatile tool to analyze gamma-ray spectra in a single shot, in which the energy and angular position of the incoming photons are recorded by observing the momenta and position of Compton scattered electrons. We present methods to reconstruct the primary spectrum from these data via machine learning and the EM Algorithm. A multi-layer fully connected neural network is used to perform the regression task of reconstructing both the double-differential spectrum and the photon energy spectrum incident with zero angular offset. We present the expected performance of these techniques, concentrating on the achievable energy resolution.}},
}