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TY - CONF AU - Azzopardi, G. AU - Muscat, A. AU - Redaelli, S. AU - Salvachua, B. AU - Valentino, G. ED - White, Karen S. ED - Brown, Kevin A. ED - Dyer, Philip S. ED - Schaa, Volker RW TI - Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning J2 - Proc. of ICALEPCS2019, New York, NY, USA, 05-11 October 2019 CY - New York, NY, USA T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 17 LA - english AB - 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 micrometers. In previous years, collimator alignments were performed semi-automatically*, requiring collimation experts to be present to oversee and control the entire process. In 2018, manual, 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 78 EP - 85 KW - alignment KW - controls KW - software KW - operation KW - collimation DA - 2020/08 PY - 2020 SN - 2226-0358 SN - 978-3-95450-209-7 DO - doi:10.18429/JACoW-ICALEPCS2019-MOCPL04 UR - https://jacow.org/icalepcs2019/papers/mocpl04.pdf ER -