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RIS citation export for WEPV007: Machine Learning Projects at the 1.5-GeV Synchroton Light Source DELTA

AU  - Schirmer, D.
AU  - Althaus, A.
AU  - Hüser, S.
AU  - Khan, S.
AU  - Schüngel, T.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Machine Learning Projects at the 1.5-GeV Synchroton Light Source DELTA
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - In recent years, several machine learning (ML) based projects have been developed to support automated monitoring and operation of the DELTA electron storage ring facility. This includes self-regulating global and local orbit correction of the stored electron beam, betatron tune feedback as well as electron transfer rate (injection) optimization. Furthermore, the implementation for a ML-based chromaticity control is currently prepared. Some of these processes were initially simulated and then successfully transferred to real machine operation. This report provides an overview of the current status of these projects.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 631
EP  - 635
KW  - controls
KW  - storage-ring
KW  - injection
KW  - synchrotron
KW  - electron
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-WEPV007
UR  - https://jacow.org/icalepcs2021/papers/wepv007.pdf
ER  -