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RIS citation export for THAL01: Machine Learning Tools Improve BESSY II Operation

AU  - Vera Ramiréz, L.
AU  - Birke, T.
AU  - Hartmann, G.
AU  - Müller, R.
AU  - Ries, M.
AU  - Schnizer, P.
AU  - Schälicke, A.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Machine Learning Tools Improve BESSY II Operation
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  - At the HZB user facility BESSY II Machine Learning (ML) technologies aim at advanced analysis, automation, explainability and performance improvements for accelerator and beamline operation. The development of these tools is intertwined with improvements of the prediction part of the digital twin instances at BESSY II [*] and the integration into the Bluesky Suite [**,***]. On the accelerator side, several use cases have recently been identified, pipelines designed and models tested. Previous studies applied Deep Reinforcement Learning (RL) to booster current and injection efficiency. RL now tackles a more demanding scenario: the mitigation of harmonic orbit perturbations induced by external civil noise sources. This paper presents methodology, design and simulation phases as well as challenges and first results. Further ML use cases under study are, among others, anomaly detection prototypes with anomaly scores for individual features.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 784
EP  - 790
KW  - experiment
KW  - network
KW  - simulation
KW  - controls
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-THAL01
UR  - https://jacow.org/icalepcs2021/papers/thal01.pdf
ER  -