| Title |
Operational Results of LHC Collimator Alignment Using Machine Learning |
| Authors |
- G. Azzopardi, A. Muscat, G. Valentino
University of Malta, Information and Communication Technology, Msida, Malta
- S. Redaelli, B. Salvachua
CERN, Geneva, Switzerland
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| Abstract |
A complex collimation system is installed in the Large Hadron Collider to protect sensitive equipment from unavoidable beam losses. The collimators are positioned close to the beam in the form of a hierarchy, which is guaranteed by precisely aligning each collimator with a precision of a few tens of micrometers. During past years, collimator alignments were performed semi-automatically*, such that collimation experts had to be present to oversee and control the alignment. In 2018, machine learning was introduced to develop a new fully-automatic alignment tool, which was used for collimator alignments throughout the year. This paper discusses how machine learning was used to automate the alignment, whilst focusing on the operational results obtained when testing the new software in the LHC. Automatically aligning the collimators decreased the alignment time at injection by a factor of three whilst maintaining the accuracy of the results.
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| Footnotes & References |
*G.Valentino et al., "Semi-automatic beam-based LHC collimator alignment", PRSTAB, no.5, 2012. |
| Paper |
download TUZZPLM1.PDF [1.544 MB / 4 pages] |
| Slides |
download TUZZPLM1_TALK.PDF [6.060 MB] |
| Export |
download ※ BibTeX
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※ EndNote |
| Conference |
IPAC2019 |
| Series |
International Particle Accelerator Conference (10th) |
| Location |
Melbourne, Australia |
| Date |
19-24 May 2019 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Mark Boland (UoM, Saskatoon, SK, Canada); Hitoshi Tanaka (KEK, Tsukuba, Japan); David Button (ANSTO, Kirrawee, NSW, Australia); Rohan Dowd (ANSTO, Kirrawee, NSW, Australia); Volker RW Schaa (GSI, Darmstadt, Germany); Eugene Tan (ANSTO, Kirrawee, NSW, Australia) |
| Online ISBN |
978-3-95450-208-0 |
| Received |
10 May 2019 |
| Accepted |
21 May 2019 |
| Issue Date |
21 June 2019 |
| DOI |
doi:10.18429/JACoW-IPAC2019-TUZZPLM1 |
| Pages |
1208-1211 |
| Copyright |
 Published 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. |
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