ICALEPCS2021 - List of Authors (Birke, T.)

Paper	Title	Page
TUPV008	Status of BlueSky Deployment at BESSY II for Machine Commissioning
	P. Schnizer, J. Bengtsson, T. Birke, J. Li, T. Mertens, M. Ries HZB, Berlin, Germany
	HZB is hosting two light sources: BESSY II and MLS. As for any light source regular commissioning task are required for monitoring machines performance next to developing and establishing new operation modes. The current modernization of the commissioning software itself is based on the BlueSky software stack. A digital twin is used as backend for testing the software itself next to providing a tuneable online machine model. We describe our users experience, exemplify commissioning tools simplifications due to the Bluesky software framework and describe the design choices made for the used digital twin.
	Poster TUPV008 [0.292 MB]
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THAL01	Machine Learning Tools Improve BESSY II Operation	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

Page

TUPV008

Status of BlueSky Deployment at BESSY II for Machine Commissioning

P. Schnizer, J. Bengtsson, T. Birke, J. Li, T. Mertens, M. Ries
HZB, Berlin, Germany

HZB is hosting two light sources: BESSY II and MLS. As for any light source regular commissioning task are required for monitoring machines performance next to developing and establishing new operation modes. The current modernization of the commissioning software itself is based on the BlueSky software stack. A digital twin is used as backend for testing the software itself next to providing a tuneable online machine model. We describe our users experience, exemplify commissioning tools simplifications due to the Bluesky software framework and describe the design choices made for the used digital twin.

Poster TUPV008 [0.292 MB]

Cite •

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

THAL01

Machine Learning Tools Improve BESSY II Operation

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)