IPAC2021 - List of Authors (Apollonio, A.)

Paper	Title	Page
MOPAB002	Risk of Halo-Induced Magnet Quenches in the HL-LHC Beam Dump Insertion	41
	J.B. Potoine, A. Apollonio, E. Belli, C. Bracco, R. Bruce, M. D’Andrea, R. García Alía, A. Lechner, G. Lerner, S. Morales Vigo, S. Redaelli, V. Rizzoglio, E. Skordis, A. Waets CERN, Meyrin, Switzerland F. Wrobel IES, Montpellier, France
	Funding: Research supported by the HL-LHC project After the High Luminosity (HL-LHC) upgrade, the LHC will be exposed to a higher risk of magnet quenches during periods of short beam lifetime. Collimators in the extraction region (IR6) assure the protection of magnets against asynchronous beam dumps, but they also intercept a fraction of the beam halo leaking from the betatron cleaning insertion. In this paper, we assess the risk of quenching nearby quadrupoles during beam lifetime drops. In particular, we present an empirical analysis of halo losses in IR6 using LHC Run 2 (2015-2018) beam loss monitor measurements. Based on these results, the halo-induced power density in magnet coils expected in HL-LHC is estimated using FLUKA Monte Carlo shower simulations.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB002
About •	paper received ※ 19 May 2021 paper accepted ※ 13 July 2021 issue date ※ 22 August 2021
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MOPAB013	Radiation to Electronics Impact on CERN LHC Operation: Run 2 Overview and HL-LHC Outlook	80
	Y.Q. Aguiar, A. Apollonio, F. Cerutti, S. Danzeca, R. García Alía, G. Lerner, D. Prelipcean, M. Sabaté-Gilarte CERN, Meyrin, Switzerland
	Funding: Research supported by the HL-LHC project After the mitigation measures implemented during Run 1 (2010-2012) and Long Shutdown 1 (LS1, 2013-2014), the number of equipment failures due to radiation effects on electronics (R2E) leading to LHC beam dumps and/or machine downtime has been sufficiently low as to yield a minor impact on the accelerator performance. During Run 2 (2015-2018) the R2E related failures per unit of integrated luminosity remained below the target value of 0.5 events/fb-1, with the sole exception of the 2015 run during which the machine commissioning took place. However, during 2018, an increase in the failure rate was observed, linked to the increased radiation levels in the dispersion suppressors of the ATLAS and CMS experimental insertions, significantly affecting the Quench Protection System located underneath the superconducting magnets in the tunnel. This work provides an overview of the Run 2 R2E events during LHC proton-proton operation, putting them in the context of the related radiation levels and equipment sensitivity, and providing an outlook for Run 3 and HL-LHC operation.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB013
About •	paper received ※ 19 May 2021 paper accepted ※ 23 July 2021 issue date ※ 23 August 2021
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MOPAB344	Machine Learning Models for Breakdown Prediction in RF Cavities for Accelerators	1068
	C. Obermair, A. Apollonio, T. Cartier-Michaud, N. Catalán Lasheras, L. Felsberger, W.L. Millar, W. Wuensch CERN, Geneva, Switzerland C. Obermair, F. Pernkopf TUG, Graz, Austria
	Radio Frequency (RF) breakdowns are one of the most prevalent limits in RF cavities for particle accelerators. During a breakdown, field enhancement associated with small deformations on the cavity surface results in electrical arcs. Such arcs degrade a passing beam and if they occur frequently, they can cause irreparable damage to the RF cavity surface. In this paper, we propose a machine learning approach to predict the occurrence of breakdowns in CERN’s Compact LInear Collider (CLIC) accelerating structures. We discuss state-of-the-art algorithms for data exploration with unsupervised machine learning, breakdown prediction with supervised machine learning, and result validation with Explainable-Artificial Intelligence (Explainable AI). By interpreting the model parameters of various approaches, we go further in addressing opportunities to elucidate the physics of a breakdown and improve accelerator reliability and operation.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB344
About •	paper received ※ 20 May 2021 paper accepted ※ 16 July 2021 issue date ※ 11 August 2021
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MOPAB345	Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC	1072
	C. Obermair, A. Apollonio, Z. Charifoulline, M. Maciejewski, A.P. Verweij CERN, Geneva, Switzerland C. Obermair, F. Pernkopf TUG, Graz, Austria
	The LHC is the world’s largest particle accelerator and uses a complex set of sophisticated and highly reliable machine protection systems to ensure a safe operation with high availability for particle physics production. The data gathered during several years of successful operation allow the use of data-driven methods to assist experts in finding anomalies in the behavior of those protection systems. In this paper, we derive a model that can extend the existing signal monitoring applications for the LHC protection systems with machine learning. Our hybrid model combines an existing threshold-based system with a SVM by using signals, manually validated by experts. Even with a limited amount of data, the SVM learns to integrate the expert knowledge and contributes to a better classification of safety-critical signals. Using this approach, we analyze historical signals of quench heaters, which are an important part of the quench protection system for superconducting magnets. Particularly, it is possible to incorporate expert decisions into the classification process and to improve the failure detection rate of the existing quench heater discharge analysis tool.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB345
About •	paper received ※ 20 May 2021 paper accepted ※ 19 July 2021 issue date ※ 01 September 2021
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TUPAB325	Data-Driven Risk Matrices for CERN’s Accelerators	2260
	T. Cartier-Michaud, A. Apollonio, G.B. Blarasin, B. Todd, J.A. Uythoven CERN, Geneva, Switzerland
	Funding: Research supported by the HL-LHC project. A risk matrix is a common tool used in risk assessment, defining risk levels with respect to the severity and probability of the occurrence of an undesired event. Risk levels can then be used for different purposes, e.g. defining subsystem reliability or personnel safety requirements. Over the history of the Large Hadron Collider (LHC), several risk matrices have been defined to guide system design. Initially, these were focused on machine protection systems, more recently these have also been used to prioritize consolidation activities. A new data-driven development of risk matrices for CERN’s accelerators is presented in this paper, based on data collected in the CERN Accelerator Fault Tracker (AFT). The data-driven approach improves the granularity of the assessment, and limits uncertainty in the risk estimation, as it is based on operational experience. In this paper the authors introduce the mathematical framework, based on operational failure data, and present the resulting risk matrix for LHC.
	Poster TUPAB325 [0.499 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB325
About •	paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 17 August 2021
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TUPAB345	Availability Modeling of the Solid-State Power Amplifiers for the CERN SPS RF Upgrade	2308
	L. Felsberger, A. Apollonio, T. Cartier-Michaud, E. Montesinos, J.C. Oliveira, J.A. Uythoven CERN, Geneva, Switzerland
	Funding: This project has received funding from the Euratom research and training programme 2019-2020 under grant agreement No 945077. As part of the LHC Injector Upgrade program a complete overhaul of the Super Proton Synchrotron Radio-Frequency (RF) system took place. New cavities have been installed for which the solid-state technology was chosen to deliver a combined RF power of 2 MW from 2560 RF amplifiers. This strategy promises high availability as the system continues operation when some of the amplifiers fail. This study quantifies the operational availability that can be achieved with this new installation. The evaluation is based on a Monte Carlo simulation of the system using the novel AvailSim4 simulation software. A model based on lifetime estimations of the RF modules is compared against data from early operational experience. Sensitivity analyses have been made, that give insight to the chosen operational scenario. With the increasing use of solid-state RF power amplifiers, the findings of this study provide a useful reference for future application of this technology in particle accelerators.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB345
About •	paper received ※ 19 May 2021 paper accepted ※ 01 July 2021 issue date ※ 19 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Risk of Halo-Induced Magnet Quenches in the HL-LHC Beam Dump Insertion

41

J.B. Potoine, A. Apollonio, E. Belli, C. Bracco, R. Bruce, M. D’Andrea, R. García Alía, A. Lechner, G. Lerner, S. Morales Vigo, S. Redaelli, V. Rizzoglio, E. Skordis, A. Waets
CERN, Meyrin, Switzerland
F. Wrobel
IES, Montpellier, France

Funding: Research supported by the HL-LHC project
After the High Luminosity (HL-LHC) upgrade, the LHC will be exposed to a higher risk of magnet quenches during periods of short beam lifetime. Collimators in the extraction region (IR6) assure the protection of magnets against asynchronous beam dumps, but they also intercept a fraction of the beam halo leaking from the betatron cleaning insertion. In this paper, we assess the risk of quenching nearby quadrupoles during beam lifetime drops. In particular, we present an empirical analysis of halo losses in IR6 using LHC Run 2 (2015-2018) beam loss monitor measurements. Based on these results, the halo-induced power density in magnet coils expected in HL-LHC is estimated using FLUKA Monte Carlo shower simulations.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB002

About •

paper received ※ 19 May 2021 paper accepted ※ 13 July 2021 issue date ※ 22 August 2021

Export •

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

MOPAB013

Radiation to Electronics Impact on CERN LHC Operation: Run 2 Overview and HL-LHC Outlook

80

Y.Q. Aguiar, A. Apollonio, F. Cerutti, S. Danzeca, R. García Alía, G. Lerner, D. Prelipcean, M. Sabaté-Gilarte
CERN, Meyrin, Switzerland

Funding: Research supported by the HL-LHC project
After the mitigation measures implemented during Run 1 (2010-2012) and Long Shutdown 1 (LS1, 2013-2014), the number of equipment failures due to radiation effects on electronics (R2E) leading to LHC beam dumps and/or machine downtime has been sufficiently low as to yield a minor impact on the accelerator performance. During Run 2 (2015-2018) the R2E related failures per unit of integrated luminosity remained below the target value of 0.5 events/fb-1, with the sole exception of the 2015 run during which the machine commissioning took place. However, during 2018, an increase in the failure rate was observed, linked to the increased radiation levels in the dispersion suppressors of the ATLAS and CMS experimental insertions, significantly affecting the Quench Protection System located underneath the superconducting magnets in the tunnel. This work provides an overview of the Run 2 R2E events during LHC proton-proton operation, putting them in the context of the related radiation levels and equipment sensitivity, and providing an outlook for Run 3 and HL-LHC operation.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB013

About •

paper received ※ 19 May 2021 paper accepted ※ 23 July 2021 issue date ※ 23 August 2021

Export •

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

MOPAB344

Machine Learning Models for Breakdown Prediction in RF Cavities for Accelerators

1068

C. Obermair, A. Apollonio, T. Cartier-Michaud, N. Catalán Lasheras, L. Felsberger, W.L. Millar, W. Wuensch
CERN, Geneva, Switzerland
C. Obermair, F. Pernkopf
TUG, Graz, Austria

Radio Frequency (RF) breakdowns are one of the most prevalent limits in RF cavities for particle accelerators. During a breakdown, field enhancement associated with small deformations on the cavity surface results in electrical arcs. Such arcs degrade a passing beam and if they occur frequently, they can cause irreparable damage to the RF cavity surface. In this paper, we propose a machine learning approach to predict the occurrence of breakdowns in CERN’s Compact LInear Collider (CLIC) accelerating structures. We discuss state-of-the-art algorithms for data exploration with unsupervised machine learning, breakdown prediction with supervised machine learning, and result validation with Explainable-Artificial Intelligence (Explainable AI). By interpreting the model parameters of various approaches, we go further in addressing opportunities to elucidate the physics of a breakdown and improve accelerator reliability and operation.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB344

About •

paper received ※ 20 May 2021 paper accepted ※ 16 July 2021 issue date ※ 11 August 2021

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

MOPAB345

Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC

1072

C. Obermair, A. Apollonio, Z. Charifoulline, M. Maciejewski, A.P. Verweij
CERN, Geneva, Switzerland
C. Obermair, F. Pernkopf
TUG, Graz, Austria

The LHC is the world’s largest particle accelerator and uses a complex set of sophisticated and highly reliable machine protection systems to ensure a safe operation with high availability for particle physics production. The data gathered during several years of successful operation allow the use of data-driven methods to assist experts in finding anomalies in the behavior of those protection systems. In this paper, we derive a model that can extend the existing signal monitoring applications for the LHC protection systems with machine learning. Our hybrid model combines an existing threshold-based system with a SVM by using signals, manually validated by experts. Even with a limited amount of data, the SVM learns to integrate the expert knowledge and contributes to a better classification of safety-critical signals. Using this approach, we analyze historical signals of quench heaters, which are an important part of the quench protection system for superconducting magnets. Particularly, it is possible to incorporate expert decisions into the classification process and to improve the failure detection rate of the existing quench heater discharge analysis tool.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB345

About •

paper received ※ 20 May 2021 paper accepted ※ 19 July 2021 issue date ※ 01 September 2021

Export •

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

TUPAB325

Data-Driven Risk Matrices for CERN’s Accelerators

2260

T. Cartier-Michaud, A. Apollonio, G.B. Blarasin, B. Todd, J.A. Uythoven
CERN, Geneva, Switzerland

Funding: Research supported by the HL-LHC project.
A risk matrix is a common tool used in risk assessment, defining risk levels with respect to the severity and probability of the occurrence of an undesired event. Risk levels can then be used for different purposes, e.g. defining subsystem reliability or personnel safety requirements. Over the history of the Large Hadron Collider (LHC), several risk matrices have been defined to guide system design. Initially, these were focused on machine protection systems, more recently these have also been used to prioritize consolidation activities. A new data-driven development of risk matrices for CERN’s accelerators is presented in this paper, based on data collected in the CERN Accelerator Fault Tracker (AFT). The data-driven approach improves the granularity of the assessment, and limits uncertainty in the risk estimation, as it is based on operational experience. In this paper the authors introduce the mathematical framework, based on operational failure data, and present the resulting risk matrix for LHC.

Poster TUPAB325 [0.499 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB325

About •

paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 17 August 2021

Export •

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

TUPAB345

Availability Modeling of the Solid-State Power Amplifiers for the CERN SPS RF Upgrade

2308

L. Felsberger, A. Apollonio, T. Cartier-Michaud, E. Montesinos, J.C. Oliveira, J.A. Uythoven
CERN, Geneva, Switzerland

Funding: This project has received funding from the Euratom research and training programme 2019-2020 under grant agreement No 945077.
As part of the LHC Injector Upgrade program a complete overhaul of the Super Proton Synchrotron Radio-Frequency (RF) system took place. New cavities have been installed for which the solid-state technology was chosen to deliver a combined RF power of 2 MW from 2560 RF amplifiers. This strategy promises high availability as the system continues operation when some of the amplifiers fail. This study quantifies the operational availability that can be achieved with this new installation. The evaluation is based on a Monte Carlo simulation of the system using the novel AvailSim4 simulation software. A model based on lifetime estimations of the RF modules is compared against data from early operational experience. Sensitivity analyses have been made, that give insight to the chosen operational scenario. With the increasing use of solid-state RF power amplifiers, the findings of this study provide a useful reference for future application of this technology in particle accelerators.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB345

About •

paper received ※ 19 May 2021 paper accepted ※ 01 July 2021 issue date ※ 19 August 2021

Export •

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