TY  - JOUR
AU  - Ma, S.
AU  - Arnold, A.
AU  - Michel, P.
AU  - Murcek, P.
AU  - Ryzhov, A.
AU  - Schaber, J.
AU  - Steinbrück, R.
AU  - Evtushenko, P.
AU  - Teichert, J.
AU  - Hillert, W.
AU  - Xiang, R.
AU  - Zhu, J.
TI  - The application of encoder–decoder neural networks in high accuracy and efficiency slit-scan emittance measurements
JO  - Nuclear instruments & methods in physics research / Section A
VL  - 1050
IS  - arXiv:2207.09144
SN  - 0167-5087
CY  - Amsterdam
PB  - North-Holland Publ. Co.
M1  - PUBDB-2023-07657
M1  - arXiv:2207.09144
SP  - 168125
PY  - 2023
AB  - 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 min to 90 s. The convolution neural networks are applied to improve the efficiency and accuracy of beamlet images processing. In order to estimate the uncertainty in the calculation of normalized emittance, we analyze the main error contributions.
KW  - SRF photo injectors (autogen)
KW  - Beam emittance (autogen)
KW  - Slit-scan (autogen)
KW  - Machine learning (autogen)
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001004484700001
DO  - DOI:10.1016/j.nima.2023.168125
UR  - https://bib-pubdb1.desy.de/record/599968
ER  -