Author: Azzopardi, G.
Paper Title Page
TUZZPLM1 Operational Results of LHC Collimator Alignment Using Machine Learning 1208
SUSPFO053   use link to see paper's listing under its alternate paper code  
  • G. Azzopardi, A. Muscat, G. Valentino
    University of Malta, Information and Communication Technology, Msida, Malta
  • S. Redaelli, B. Salvachua
    CERN, Geneva, Switzerland
  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.
*G.Valentino et al., "Semi-automatic beam-based LHC collimator alignment", PRSTAB, no.5, 2012.
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About • paper received ※ 10 May 2019       paper accepted ※ 21 May 2019       issue date ※ 21 June 2019  
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