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DC Field | Value | Language |
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dc.contributor.author | Azzopardi, Gabriella | - |
dc.contributor.author | Muscat, Adrian | - |
dc.contributor.author | Valentino, Gianluca | - |
dc.contributor.author | Redaelli, Stefano | - |
dc.contributor.author | Salvachua, Belen | - |
dc.date.accessioned | 2020-07-17T06:17:04Z | - |
dc.date.available | 2020-07-17T06:17:04Z | - |
dc.date.issued | 2019-05 | - |
dc.identifier.citation | Azzopardi, G., Muscat, A., Redaelli, S., Salvachua, B., & Valentino, G. (2019, June). Operational Results of LHC Collimator Alignment using Machine Learning. In 10th Int. Particle Accelerator Conf.(IPAC'19), Melbourne, Australia, 19-24 May 2019 (pp. 1208-1211). JACOW Publishing, Geneva, Switzerland. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/58842 | - |
dc.description.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. | 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.title | Operational results of 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 | IPAC2019 : 10th Int. Partile Accelerator Conference | en_GB |
dc.bibliographicCitation.conferenceplace | Melbourne, Australia, 19/24/05/2019 | 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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tuzzplm1.pdf | 1.51 MB | Adobe PDF | View/Open |
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