IPAC2022 - List of Authors (Apollonio, A.)

Paper	Title	Page
MOPOPT040	Summary of the Post-Long Shutdown 2 LHC Hardware Commissioning Campaign	335
	A. Apollonio, O.Ø. Andreassen, A. Antoine, T. Argyropoulos, M.C. Bastos, M. Bednarek, B. Bordini, K. Brodzinski, A. Calia, Z. Charifoulline, G.-J. Coelingh, G. D’Angelo, D. Delikaris, R. Denz, L. Fiscarelli, V. Froidbise, M.A. Galilée, J.C. Garnier, R. Gorbonosov, P. Hagen, M. Hostettler, D. Jacquet, S. Le Naour, D. Mirarchi, V. Montabonnet, B.I. Panev, T.H.B. Persson, T. Podzorny, M. Pojer, E. Ravaioli, F. Rodriguez-Mateos, A.P. Siemko, M. Solfaroli, J. Spasic, A. Stanisz, J. Steckert, R. Steerenberg, S. Sudak, H. Thiesen, E. Todesco, G. Trad, J.A. Uythoven, S. Uznanski, A.P. Verweij, J. Wenninger, G.P. Willering, D. Wollmann, S. Yammine CERN, Meyrin, Switzerland V. Vizziello INFN/LASA, Segrate (MI), Italy
	In this contribution we provide a summary of the LHC hardware commissioning campaign following the second CERN Long Shutdown (LS2), initially targeting the nominal LHC energy of 7 TeV. A summary of the test procedures and tools used for testing the LHC superconducting circuits is given, together with statistics on the successful test execution. The paper then focuses on the experience and observations during the main dipole training campaign, describing the encountered problems, the related analysis and mitigation measures, ultimately leading to the decision to reduce the energy target to 6.8 TeV. The re-commissioning of two powering sectors, following the identified problems, is discussed in detail. The paper concludes with an outlook to the future hardware commissioning campaigns, discussing the lessons learnt and possible strategies moving forward.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT040
About •	Received ※ 08 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 27 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPOMS044	Implications and Mitigation of Radiation Effects on the CERN SPS Operation during 2021	740
	Y.Q. Aguiar, A. Apollonio, K. Biłko, M. Brucoli, M. Cecchetto, S. Danzeca, R. García Alía, T. Ladzinski, G. Lerner, J.B. Potoine, A. Zimmaro CERN, Meyrin, Switzerland
	During the Long Shutdown 2 (LS2, 2019-2020), the CERN accelerator complex has undergone major upgrades, mainly in preparation for the High-Luminosity (HL) LHC era, the ultimate capacity for its physics production. Therefore, several novel equipment and systems were designed and deployed throughout the accelerator complex. To comply with the radiation level specifications and avoid machine downtime due to radiation effects, the electronics systems exposed to radiation need to follow Radiation Hardness Assurance (RHA) methodologies developed and validated by the Radiation to Electronics (R2E) project at CERN. However, the establishment of such procedures is not yet fully implemented in the LHC injector chain, and some R2E failures were detected in the SPS during the 2021 operation. This work is devoted to describing and analysing the R2E failures and their impact on operation, in the context of the related radiation levels and equipment sensitivity.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOMS044
About •	Received ※ 07 June 2022 — Revised ※ 21 June 2022 — Accepted ※ 26 June 2022 — Issue date ※ 08 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPOMS046	Reliability Analysis of the HL-LHC Energy Extraction System	747
	M.R. Blaszkiewicz, A. Apollonio, T. Cartier-Michaud, B.I. Panev, M. Pojer, D. Wollmann CERN, Meyrin, Switzerland
	The energy extraction systems for the protection of the new HL-LHC superconducting magnet circuits are based on vacuum breakers. This technology allows to significantly reduce the switch opening time and increases the overall system reliability with reduced maintenance needs. This paper presents the results of detailed reliability studies performed on these new energy extraction systems. The study quantifies the risk of a failure which prevents correct protection of a magnet circuit and identifies the most critical components of the system. For this, the model considers factors such as block or component level failure probabilities, different maintenance strategies and repair procedures. The reliability simulations have been performed with AvailSim4, a novel Monte Carlo code for availability and reliability simulations. The results are compared with the system reliability requirements and provides insights into the most critical components.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOMS046
About •	Received ※ 08 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 07 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPOMS054	Data Augmentation for Breakdown Prediction in CLIC RF Cavities	1553
	H.S. Bovbjerg, M. Shen, Z.H. Tan Aalborg University, Aalborg, Denmark A. Apollonio, H.S. Bovbjerg, T. Cartier-Michaud, W.L. Millar, C. Obermair, D. Wollmann CERN, Meyrin, Switzerland C. Obermair TUG, Graz, Austria
	One of the primary limitations on the achievable accelerating gradient in normal-conducting accelerator cavities is the occurrence of vacuum arcs, also known as RF breakdowns. A recent study on experimental data from the CLIC XBOX2 test stand at CERN proposes the use of supervised machine learning methods for predicting RF breakdowns. As RF breakdowns occur relatively infrequently during operation, the majority of the data was instead comprised of non-breakdown pulses. This phenomenon is known in the field of machine learning as class imbalance and is problematic for the training of the models. This paper proposes the use of data augmentation methods to generate synthetic data to counteract this problem. Different data augmentation methods like random transformations and pattern mixing are applied to the experimental data from the XBOX2 test stand, and their efficiency is compared.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOMS054
About •	Received ※ 08 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 13 June 2022 — Issue date ※ 15 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Summary of the Post-Long Shutdown 2 LHC Hardware Commissioning Campaign

335

A. Apollonio, O.Ø. Andreassen, A. Antoine, T. Argyropoulos, M.C. Bastos, M. Bednarek, B. Bordini, K. Brodzinski, A. Calia, Z. Charifoulline, G.-J. Coelingh, G. D’Angelo, D. Delikaris, R. Denz, L. Fiscarelli, V. Froidbise, M.A. Galilée, J.C. Garnier, R. Gorbonosov, P. Hagen, M. Hostettler, D. Jacquet, S. Le Naour, D. Mirarchi, V. Montabonnet, B.I. Panev, T.H.B. Persson, T. Podzorny, M. Pojer, E. Ravaioli, F. Rodriguez-Mateos, A.P. Siemko, M. Solfaroli, J. Spasic, A. Stanisz, J. Steckert, R. Steerenberg, S. Sudak, H. Thiesen, E. Todesco, G. Trad, J.A. Uythoven, S. Uznanski, A.P. Verweij, J. Wenninger, G.P. Willering, D. Wollmann, S. Yammine
CERN, Meyrin, Switzerland
V. Vizziello
INFN/LASA, Segrate (MI), Italy

In this contribution we provide a summary of the LHC hardware commissioning campaign following the second CERN Long Shutdown (LS2), initially targeting the nominal LHC energy of 7 TeV. A summary of the test procedures and tools used for testing the LHC superconducting circuits is given, together with statistics on the successful test execution. The paper then focuses on the experience and observations during the main dipole training campaign, describing the encountered problems, the related analysis and mitigation measures, ultimately leading to the decision to reduce the energy target to 6.8 TeV. The re-commissioning of two powering sectors, following the identified problems, is discussed in detail. The paper concludes with an outlook to the future hardware commissioning campaigns, discussing the lessons learnt and possible strategies moving forward.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT040

About •

Received ※ 08 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 27 June 2022

Cite •

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

MOPOMS044

Implications and Mitigation of Radiation Effects on the CERN SPS Operation during 2021

740

Y.Q. Aguiar, A. Apollonio, K. Biłko, M. Brucoli, M. Cecchetto, S. Danzeca, R. García Alía, T. Ladzinski, G. Lerner, J.B. Potoine, A. Zimmaro
CERN, Meyrin, Switzerland

During the Long Shutdown 2 (LS2, 2019-2020), the CERN accelerator complex has undergone major upgrades, mainly in preparation for the High-Luminosity (HL) LHC era, the ultimate capacity for its physics production. Therefore, several novel equipment and systems were designed and deployed throughout the accelerator complex. To comply with the radiation level specifications and avoid machine downtime due to radiation effects, the electronics systems exposed to radiation need to follow Radiation Hardness Assurance (RHA) methodologies developed and validated by the Radiation to Electronics (R2E) project at CERN. However, the establishment of such procedures is not yet fully implemented in the LHC injector chain, and some R2E failures were detected in the SPS during the 2021 operation. This work is devoted to describing and analysing the R2E failures and their impact on operation, in the context of the related radiation levels and equipment sensitivity.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOMS044

About •

Received ※ 07 June 2022 — Revised ※ 21 June 2022 — Accepted ※ 26 June 2022 — Issue date ※ 08 July 2022

Cite •

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

MOPOMS046

Reliability Analysis of the HL-LHC Energy Extraction System

747

M.R. Blaszkiewicz, A. Apollonio, T. Cartier-Michaud, B.I. Panev, M. Pojer, D. Wollmann
CERN, Meyrin, Switzerland

The energy extraction systems for the protection of the new HL-LHC superconducting magnet circuits are based on vacuum breakers. This technology allows to significantly reduce the switch opening time and increases the overall system reliability with reduced maintenance needs. This paper presents the results of detailed reliability studies performed on these new energy extraction systems. The study quantifies the risk of a failure which prevents correct protection of a magnet circuit and identifies the most critical components of the system. For this, the model considers factors such as block or component level failure probabilities, different maintenance strategies and repair procedures. The reliability simulations have been performed with AvailSim4, a novel Monte Carlo code for availability and reliability simulations. The results are compared with the system reliability requirements and provides insights into the most critical components.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOMS046

About •

Received ※ 08 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 07 July 2022

Cite •

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

TUPOMS054

Data Augmentation for Breakdown Prediction in CLIC RF Cavities

1553

H.S. Bovbjerg, M. Shen, Z.H. Tan
Aalborg University, Aalborg, Denmark
A. Apollonio, H.S. Bovbjerg, T. Cartier-Michaud, W.L. Millar, C. Obermair, D. Wollmann
CERN, Meyrin, Switzerland
C. Obermair
TUG, Graz, Austria

One of the primary limitations on the achievable accelerating gradient in normal-conducting accelerator cavities is the occurrence of vacuum arcs, also known as RF breakdowns. A recent study on experimental data from the CLIC XBOX2 test stand at CERN proposes the use of supervised machine learning methods for predicting RF breakdowns. As RF breakdowns occur relatively infrequently during operation, the majority of the data was instead comprised of non-breakdown pulses. This phenomenon is known in the field of machine learning as class imbalance and is problematic for the training of the models. This paper proposes the use of data augmentation methods to generate synthetic data to counteract this problem. Different data augmentation methods like random transformations and pattern mixing are applied to the experimental data from the XBOX2 test stand, and their efficiency is compared.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOMS054

About •

Received ※ 08 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 13 June 2022 — Issue date ※ 15 June 2022

Cite •

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