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@ARTICLE{Ma:481076,
author = {Ma, S. and Arnold, A. and Michel, P. and Murcek, P. and
Ryzhov, A. and Schaber, J. and Steinbrück, R. and
Evtushenko, P. and Teichert, Jan-Hendrik and Hillert, W. and
Xiang, R. and Zhu, J.},
title = {{T}he application of machine learning in high accuracy and
efficiency slit-scan emittance measurements},
reportid = {PUBDB-2022-04155, arXiv:2207.09144},
year = {2022},
abstract = {A superconducting radio-frequency (SRF) photo injector is
in operation at the electron linac for beams with high
brilliance and low emittance (ELBE) radiation center and
generates continuous wave (CW) electron beams with high
average current and high brightness for user operation since
2018. The speed of emittance measurement at the SRF gun
beamline can be increased by improving the slit-scan system,
thus the measurement time for one phase space mapping can be
shortened from about 15 minutes to 90 seconds. A parallel
algorithm and machine learning have been used to reduce the
beamlet image noise. In order to estimate the uncertainty in
the calculation of normalized emittance, we analyze the main
error contributions such as slit position uncertainty, image
noise, space charge effects and energy measurement
inaccuracy.},
cin = {ZEU-NIC},
cid = {I:(DE-H253)ZEU-NIC-20120731},
pnm = {611 - Fundamental Particles and Forces (POF4-611)},
pid = {G:(DE-HGF)POF4-611},
experiment = {EXP:(DE-MLZ)NOSPEC-20140101},
typ = {PUB:(DE-HGF)25},
eprint = {2207.09144},
howpublished = {arXiv:2207.09144},
archivePrefix = {arXiv},
SLACcitation = {$\%\%CITATION$ = $arXiv:2207.09144;\%\%$},
doi = {10.3204/PUBDB-2022-04155},
url = {https://bib-pubdb1.desy.de/record/481076},
}