IPAC2019 - List of Authors (Muscat, A.)

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.
	Slides TUZZPLM1 [6.060 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUZZPLM1
About •	paper received ※ 10 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

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.

Slides TUZZPLM1 [6.060 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUZZPLM1

About •

paper received ※ 10 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)