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DC Field | Value | Language |
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dc.contributor.author | Azzopardi, Gabriella | - |
dc.contributor.author | Valentino, Gianluca | - |
dc.contributor.author | Salvachua, Belen | - |
dc.contributor.author | Redaelli, Stefano | - |
dc.contributor.author | Muscat, Adrian | - |
dc.date.accessioned | 2020-07-20T06:22:02Z | - |
dc.date.available | 2020-07-20T06:22:02Z | - |
dc.date.issued | 2019-10 | - |
dc.identifier.citation | Azzopardi, G., Valentino, G., Salvachua, B., Redaelli, S., & Muscat, A. (2019). Software architecture for automatic LHC collimator alignment using machine learning. ICALEPCS2019, New York. 0-7. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/58902 | - |
dc.description.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 micrometres. In previous years, collimator alignments were performed semi-automatically, requiring collimation experts to be present to oversee and control the entire process. In 2018, 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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | JACoW | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Large Hadron Collider (France and Switzerland) | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Software architecture | en_GB |
dc.subject | Collimators (Optical instrument) | en_GB |
dc.title | Software architecture for automatic LHC collimator alignment using machine learning | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | ICALEPCS2019 | en_GB |
dc.bibliographicCitation.conferenceplace | New York, NY, USA, 5-11/10/2019 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - FacICTCCE |
Files in This Item:
File | Description | Size | Format | |
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mocpl04.pdf | 6.06 MB | Adobe PDF | View/Open |
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