Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/58904
Title: Data-driven cross-talk modeling of beam losses in LHC collimators
Authors: Azzopardi, Gabriella
Salvachua, Belen
Valentino, Gianluca
Keywords: Large Hadron Collider (France and Switzerland)
Machine learning
Issue Date: 2019-08
Publisher: American Physical Society
Citation: Azzopardi, G., Salvachua, B., & Valentino, G. (2019). Data-driven cross-talk modeling of beam losses in LHC collimators. Physical Review Accelerators and Beams, 22(8), 083002.
Abstract: The Large Hadron Collider at CERN is equipped with a collimation system to intercept potentially dangerous beam halo particles before they damage its sensitive equipment. The collimator settings are determined following a beam-based alignment procedure, in which the collimator jaws are moved towards the beam until losses appear in the beam loss monitors. When the collimator reaches the beam envelope, beam losses propagate mainly in the direction of the beam and are, therefore, also observed by other nearby beam loss monitors. This phenomenon is known as cross talk. Due to this, collimators are aligned sequentially to be able to identify which losses are generated by which collimator, such that any cross talk across beam loss monitors positioned close to each other is avoided. This paper seeks to quantify the levels of cross-talk observed by beam loss monitors when multiple collimators are moving, to be able to determine the actual beam loss signals generated by their corresponding collimators. The results obtained successfully predicted the amount of cross-talk observed for each of the cases tested in this study. This was then extended to predict loss map case studies and the proton impacts at each collimator by comparing them to simulations.
URI: https://www.um.edu.mt/library/oar/handle/123456789/58904
Appears in Collections:Scholarly Works - FacICTCCE

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