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Title: Operational results of LHC collimator alignment using machine learning
Authors: Azzopardi, Gabriella
Muscat, Adrian
Valentino, Gianluca
Redaelli, Stefano
Salvachua, Belen
Keywords: Large Hadron Collider (France and Switzerland)
Machine learning
Issue Date: 2019-05
Publisher: JACoW
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.
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.
Appears in Collections:Scholarly Works - FacICTCCE

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