000481076 001__ 481076
000481076 005__ 20231210012935.0
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000481076 0247_ $$2arXiv$$aarXiv:2207.09144
000481076 0247_ $$2datacite_doi$$a10.3204/PUBDB-2022-04155
000481076 037__ $$aPUBDB-2022-04155
000481076 041__ $$aEnglish
000481076 088__ $$2arXiv$$aarXiv:2207.09144
000481076 1001_ $$0P:(DE-HGF)0$$aMa, S.$$b0$$eCorresponding author
000481076 245__ $$aThe application of machine learning in high accuracy and efficiency slit-scan emittance measurements
000481076 260__ $$c2022
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000481076 520__ $$aA superconducting radio-frequency (SRF) photo injector is in operation at the electron linac for beams with high brilliance and low emittance (ELBE) radiation center and generates continuous wave (CW) electron beams with high average current and high brightness for user operation since 2018. The speed of emittance measurement at the SRF gun beamline can be increased by improving the slit-scan system, thus the measurement time for one phase space mapping can be shortened from about 15 minutes to 90 seconds. A parallel algorithm and machine learning have been used to reduce the beamlet image noise. In order to estimate the uncertainty in the calculation of normalized emittance, we analyze the main error contributions such as slit position uncertainty, image noise, space charge effects and energy measurement inaccuracy.
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000481076 7001_ $$aArnold, A.$$b1
000481076 7001_ $$aMichel, P.$$b2
000481076 7001_ $$0P.Murcek.1$$aMurcek, P.$$b3
000481076 7001_ $$0A.V.Ryzhov.1$$aRyzhov, A.$$b4
000481076 7001_ $$0J.Schaber.1$$aSchaber, J.$$b5
000481076 7001_ $$aSteinbrück, R.$$b6
000481076 7001_ $$aEvtushenko, P.$$b7
000481076 7001_ $$0P:(DE-H253)PIP1022109$$aTeichert, Jan-Hendrik$$b8$$eCorresponding author
000481076 7001_ $$0P:(DE-HGF)0$$aHillert, W.$$b9
000481076 7001_ $$0R.Xiang.1$$aXiang, R.$$b10
000481076 7001_ $$0P:(DE-HGF)0$$aZhu, J.$$b11
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000481076 9141_ $$y2022
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