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https://doi.org/10.18429/JACoW-ICALEPCS2019-MOCPL04
Title Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning
Authors
  • G. Azzopardi, S. Redaelli, B. Salvachua
    CERN, Meyrin, Switzerland
  • A. Muscat, G. Valentino
    University of Malta, Information and Communication Technology, Msida, Malta
Abstract The Large Hadron Collider at CERN relies on a collimation system to absorb unavoidable beam losses before they reach the superconducting magnets. The collimators are positioned close to the beam in a transverse setting hierarchy achieved by aligning each collimator with a precision of a few tens of micrometers. In previous years, collimator alignments were performed semi-automatically*, requiring collimation experts to be present to oversee and control the entire process. In 2018, manual, expert control of the alignment procedure was replaced by dedicated machine learning algorithms, and this new software was used for collimator alignments throughout the year. This paper gives an overview of the software re-design required to achieve fully automatic collimator alignments, describing in detail the software architecture and controls systems involved. Following this successful deployment, this software will be used in the future as the default alignment software for the LHC.
Footnotes & References *G. Valentino et al., "Semi-automatic beam-based LHC collimator alignment", Physical Review Special Topics-Accelerators and Beams vol. 15, no. 5, 2012.
Paper download MOCPL04.PDF [6.274 MB / 8 pages]
Slides download MOCPL04_TALK.PDF [5.933 MB]
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Conference ICALEPCS2019
Series International Conference on Accelerator and Large Experimental Physics Control Systems (17th)
Location New York, NY, USA
Date 05-11 October 2019
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Karen S. White (ORNL, Oak Ridge, TN, USA); Kevin A. Brown (BNL, Upton, NY, USA); Philip S. Dyer (BNL, Upton, NY, USA); Volker RW Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-209-7
Online ISSN 2226-0358
Received 28 September 2019
Accepted 09 October 2019
Issue Date 30 August 2020
DOI doi:10.18429/JACoW-ICALEPCS2019-MOCPL04
Pages 78-85
Copyright
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.