ICALEPCS2021 - List of Authors (Azzopardi, G.)

Paper	Title	Page
WEPV016	The Automatic LHC Collimator Beam-Based Alignment Software Package	659
	G. Azzopardi, B. Salvachua CERN, Geneva, Switzerland G. Valentino University of Malta, Information and Communication Technology, Msida, Malta
	The Large Hadron Collider (LHC) at CERN makes use of a complex collimation system to protect its sensitive equipment from unavoidable beam losses. The collimators are positioned around the beam respecting a strict transverse hierarchy. The position of each collimator is determined following a beam-based alignment technique which determines the required jaw settings for optimum performance. During the LHC Run 2 (2015-2018), a new automatic alignment software package was developed and used for collimator alignments throughout 2018. This paper discusses the usability and flexibility of this new package describing the implementation in detail, as well as the latest improvements and features in preparation for Run 3 starting in 2022. The automation has already successfully decreased the alignment time by 70% in 2018* and this paper explores how to further exploit this software package. Its implementation provides a solid foundation to automatically align any new collimation configurations in the future, as well as allows for further analysis and upgrade of its individual modules. G.Azzopardi, et al"Software Architecture for Automatic LHC Collimator Alignment using ML",ICALEPCS19. *G.Azzopardi, et al"Operational Results on the Fully-Automatic LHC Collimator Alignment",PRAB19.
	Poster WEPV016 [0.443 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV016
About •	Received ※ 07 October 2021 Revised ※ 22 October 2021 Accepted ※ 22 December 2021 Issue date ※ 26 December 2021
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPV012	LHC Collimation Controls System for Run III Operation	888
	G. Azzopardi, M. Di Castro, S. Redaelli, B. Salvachua, M. Solfaroli Camillocci CERN, Geneva, Switzerland G. Valentino University of Malta, Information and Communication Technology, Msida, Malta
	The Large Hadron Collider (LHC) collimation system is designed to protect the machine against unavoidable beam losses. The collimation system for the LHC Run 3, starting in 2022, consists of more than 100 movable collimators located along the 27 km long ring and in the transfer lines. The cleaning performance and machine protection role of the system critically depend on the accurate positioning of the collimator jaws. The collimation control system in place enables remote control and appropriate diagnostics of the relevant parameters. This ensures that the collimators dynamically follow optimum settings in all phases of the LHC operational cycle. In this paper, an overview of the top-level software tools available for collimation control from the control room is given. These tools range from collimator alignment applications to generation tools for collimator settings, as well as collimator scans, settings checks and machine protection sequences. Amongst these tools the key upgrades and newly introduced tools for the Run 3 are presented.
	Poster THPV012 [5.521 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV012
About •	Received ※ 07 October 2021 Revised ※ 25 October 2021 Accepted ※ 16 December 2021 Issue date ※ 01 March 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPV040	New Machine Learning Model Application for the Automatic LHC Collimator Beam-Based Alignment	953
	G. Azzopardi CERN, Geneva, Switzerland G. Ricci Sapienza University of Rome, Rome, Italy
	A collimation system is installed in the Large Hadron Collider (LHC) to protect its sensitive equipment from unavoidable beam losses. An alignment procedure determines the settings of each collimator, by moving the collimator jaws towards the beam until a characteristic loss pattern, consisting of a sharp rise followed by a slow decay, is observed in downstream beam loss monitors. This indicates that the collimator jaw intercepted the reference beam halo and is thus aligned to the beam. The latest alignment software introduced in 2018 relies on supervised machine learning (ML) to detect such spike patterns in real-time. This enables the automatic alignment of the collimators with a significant reduction in the alignment time. This paper analyses the first-use performance of this new software focusing on solutions to the identified bottleneck caused by waiting a fixed duration of time when detecting spikes. It is proposed to replace the supervised ML model with a Long-Short Term Memory model able to detect spikes in time windows of varying lengths, waiting for a variable duration of time determined by the spike itself. This will allow to further speed up the automatic alignment. G. Azzopardi et al., "Automatic spike detection in beam loss signals for LHC collimator alignment", NIMA 2019. **G. Azzopardi et al., "Operational Results of LHC collimator alignment using ML", IPAC’19.
	Poster THPV040 [0.894 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV040
About •	Received ※ 08 October 2021 Accepted ※ 21 November 2021 Issue date ※ 10 December 2021
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

The Automatic LHC Collimator Beam-Based Alignment Software Package

659

G. Azzopardi, B. Salvachua
CERN, Geneva, Switzerland
G. Valentino
University of Malta, Information and Communication Technology, Msida, Malta

The Large Hadron Collider (LHC) at CERN makes use of a complex collimation system to protect its sensitive equipment from unavoidable beam losses. The collimators are positioned around the beam respecting a strict transverse hierarchy. The position of each collimator is determined following a beam-based alignment technique which determines the required jaw settings for optimum performance. During the LHC Run 2 (2015-2018), a new automatic alignment software package was developed and used for collimator alignments throughout 2018*. This paper discusses the usability and flexibility of this new package describing the implementation in detail, as well as the latest improvements and features in preparation for Run 3 starting in 2022. The automation has already successfully decreased the alignment time by 70% in 2018** and this paper explores how to further exploit this software package. Its implementation provides a solid foundation to automatically align any new collimation configurations in the future, as well as allows for further analysis and upgrade of its individual modules.
*G.Azzopardi, et al"Software Architecture for Automatic LHC Collimator Alignment using ML",ICALEPCS19.
**G.Azzopardi, et al"Operational Results on the Fully-Automatic LHC Collimator Alignment",PRAB19.

Poster WEPV016 [0.443 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV016

About •

Received ※ 07 October 2021 Revised ※ 22 October 2021 Accepted ※ 22 December 2021 Issue date ※ 26 December 2021

Cite •

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

THPV012

LHC Collimation Controls System for Run III Operation

888

G. Azzopardi, M. Di Castro, S. Redaelli, B. Salvachua, M. Solfaroli Camillocci
CERN, Geneva, Switzerland
G. Valentino
University of Malta, Information and Communication Technology, Msida, Malta

The Large Hadron Collider (LHC) collimation system is designed to protect the machine against unavoidable beam losses. The collimation system for the LHC Run 3, starting in 2022, consists of more than 100 movable collimators located along the 27 km long ring and in the transfer lines. The cleaning performance and machine protection role of the system critically depend on the accurate positioning of the collimator jaws. The collimation control system in place enables remote control and appropriate diagnostics of the relevant parameters. This ensures that the collimators dynamically follow optimum settings in all phases of the LHC operational cycle. In this paper, an overview of the top-level software tools available for collimation control from the control room is given. These tools range from collimator alignment applications to generation tools for collimator settings, as well as collimator scans, settings checks and machine protection sequences. Amongst these tools the key upgrades and newly introduced tools for the Run 3 are presented.

Poster THPV012 [5.521 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV012

About •

Received ※ 07 October 2021 Revised ※ 25 October 2021 Accepted ※ 16 December 2021 Issue date ※ 01 March 2022

Cite •

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

THPV040

New Machine Learning Model Application for the Automatic LHC Collimator Beam-Based Alignment

953

G. Azzopardi
CERN, Geneva, Switzerland
G. Ricci
Sapienza University of Rome, Rome, Italy

A collimation system is installed in the Large Hadron Collider (LHC) to protect its sensitive equipment from unavoidable beam losses. An alignment procedure determines the settings of each collimator, by moving the collimator jaws towards the beam until a characteristic loss pattern, consisting of a sharp rise followed by a slow decay, is observed in downstream beam loss monitors. This indicates that the collimator jaw intercepted the reference beam halo and is thus aligned to the beam. The latest alignment software introduced in 2018 relies on supervised machine learning (ML) to detect such spike patterns in real-time*. This enables the automatic alignment of the collimators with a significant reduction in the alignment time**. This paper analyses the first-use performance of this new software focusing on solutions to the identified bottleneck caused by waiting a fixed duration of time when detecting spikes. It is proposed to replace the supervised ML model with a Long-Short Term Memory model able to detect spikes in time windows of varying lengths, waiting for a variable duration of time determined by the spike itself. This will allow to further speed up the automatic alignment.
*G. Azzopardi et al., "Automatic spike detection in beam loss signals for LHC collimator alignment", NIMA 2019.
**G. Azzopardi et al., "Operational Results of LHC collimator alignment using ML", IPAC’19.

Poster THPV040 [0.894 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV040

About •

Received ※ 08 October 2021 Accepted ※ 21 November 2021 Issue date ※ 10 December 2021

Cite •

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