ICALEPCS2021 - List of Keywords (ISOL)

Paper	Title	Other Keywords	Page
THAL01	Machine Learning Tools Improve BESSY II Operation	experiment, network, simulation, controls	784
	L. Vera Ramiréz, T. Birke, G. Hartmann, R. Müller, M. Ries, A. Schälicke, P. Schnizer HZB, Berlin, Germany
	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. [] P. Schnizer et. al, IPAC21 [] D. Allan, T. Caswell, S. Campbell and M. Rakitin, Synchrot. Radiat. News 32 19-22, 2019 [*] W. Smith et. al, this conference
	Slides THAL01 [9.849 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL01
About •	Received ※ 08 October 2021 Revised ※ 24 October 2021 Accepted ※ 21 November 2021 Issue date ※ 29 January 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

THAL01

Machine Learning Tools Improve BESSY II Operation

experiment, network, simulation, controls

784

L. Vera Ramiréz, T. Birke, G. Hartmann, R. Müller, M. Ries, A. Schälicke, P. Schnizer
HZB, Berlin, Germany

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.
[*] P. Schnizer et. al, IPAC21
[**] D. Allan, T. Caswell, S. Campbell and M. Rakitin, Synchrot. Radiat. News 32 19-22, 2019
[***] W. Smith et. al, this conference

Slides THAL01 [9.849 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL01

About •

Received ※ 08 October 2021 Revised ※ 24 October 2021 Accepted ※ 21 November 2021 Issue date ※ 29 January 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)