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Title: Software architecture for automatic LHC collimator alignment using machine learning
Authors: Azzopardi, Gabriella
Valentino, Gianluca
Salvachua, Belen
Redaelli, Stefano
Muscat, Adrian
Keywords: Large Hadron Collider (France and Switzerland)
Machine learning
Software architecture
Collimators (Optical instrument)
Issue Date: 2019-10
Publisher: JACoW
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.
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.
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