IPAC2022 - List of Authors (Barnes, M.J.)

Paper	Title	Page
TUPOST044	Fortune Telling or Physics Prediction? Deep Learning for On-Line Kicker Temperature Forecasting	957
	F.M. Velotti, M.J. Barnes, B. Goddard, I. Revuelta CERN, Meyrin, Switzerland
	The injection kicker system MKP of the Super Proton Synchrotron SPS at CERN is composed of 4 kicker tanks. The MKP-L tank provides additional kick needed to inject 26 GeV Large Hadron Collider LHC 25 ns type beams. This device has been a limiting factor for operation with high intensity, due to the magnet’s broadband beam coupling impedance and consequent beam induced heating. To optimise the usage of the SPS and avoid idle (kicker cooling) time, studies were conducted to develop a recurrent deep learning model that could predict the measured temperature evolution of the MKP-L, using the beam conditions and temperature history as input. In a second stage, the ferrite temperature is also estimated putting together the external temperature predictions from accurate thermo-mechanical simulations of the kicker magnet. In this paper, the methodology is described and details of the neural network architecture used, together with the implementation of an ad-hoc loss function, are given. The results applied to the SPS 2021 operational data are presented.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST044
About •	Received ※ 06 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 18 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPOST045	Overview of the Machine Learning and Numerical Optimiser Applications on Beam Transfer Systems for LHC and Its Injectors	961
	F.M. Velotti, M.J. Barnes, E. Carlier, Y. Dutheil, M.A. Fraser, B. Goddard, N. Magnin, R.L. Ramjiawan, E. Renner, P. Van Trappen CERN, Meyrin, Switzerland E. Waagaard Uppsala University, Uppsala, Sweden
	Machine learning and numerical optimisation algorithms are getting more and more popular in the accelerator physics community and, thanks to the computing power available, their application in daily operation more likely. In the CERN accelerator complex, and specifically on the beam transfer systems, many promising exploitation of these numerical tools have been put in place in the last years. Some of the state-of-the-art machine learning models have been explored and used to solve problems that were never fully addressed in the past. In this paper, the most recent results of application of machine learning and numerical optimisation for injection, extraction and transfer of beam from machine and to experimental areas are presented. An overview of the possible next steps and shortcomings is finally discussed.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST045
About •	Received ※ 06 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 10 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEIYGD1	Achievements and Performance Prospects of the Upgraded LHC Injectors	1610
	V. Kain, S.C.P. Albright, R. Alemany-Fernández, M.E. Angoletta, F. Antoniou, T. Argyropoulos, F. Asvesta, B. Balhan, M.J. Barnes, D. Barrientos, H. Bartosik, P. Baudrenghien, G. Bellodi, N. Biancacci, A. Boccardi, J.C.C.M. Borburgh, C. Bracco, E. Carlier, D.G. Cotte, J. Coupard, H. Damerau, G.P. Di Giovanni, A. Findlay, M.A. Fraser, A. Funken, B. Goddard, G. Hagmann, K. Hanke, A. Huschauer, M. Jaussi, I. Karpov, T. Koevener, D. Küchler, J.-B. Lallement, A. Lasheen, T.E. Levens, K.S.B. Li, A.M. Lombardi, N. Madysa, E. Mahner, M. Meddahi, L. Mether, B. Mikulec, J.C. Molendijk, E. Montesinos, D. Nisbet, F.-X. Nuiry, G. Papotti, K. Paraschou, F. Pedrosa, T. Prebibaj, S. Prodon, D. Quartullo, E. Renner, F. Roncarolo, G. Rumolo, B. Salvant, M. Schenk, R. Scrivens, E.N. Shaposhnikova, P.K. Skowroński, A. Spierer, F. Tecker, D. Valuch, F.M. Velotti, R. Wegner, C. Zannini CERN, Meyrin, Switzerland
	To provide HL-LHC performance, the CERN LHC injector chain underwent a major upgrade during an almost 2-year-long shutdown. In the first half of 2021 the injectors were gradually re-started with the aim to reach at least pre-shutdown parameters for LHC as well as for fixed target beams. The strategy of the commissioning across the complex, a summary of the many challenges and finally the achievements will be presented. Several lessons were learned and have been integrated to define the strategy for the performance ramp-up over the coming years. Remaining limitations and prospects for LHC beam parameters at the exit of the LHC injector chain in the years to come will be discussed. Finally, the emerging need for improved operability of the CERN complex will be addressed, with a description of the first efforts to meet the availability and flexibility requirements of the HL-LHC era while at the same time maximizing fixed target physics output.
	Slides WEIYGD1 [5.905 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEIYGD1
About •	Received ※ 08 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 09 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPOTK043	Mitigation of High Voltage Breakdown of the Beam Screen of a CERN SPS Injection Kicker Magnet	2868
	M.J. Barnes, W. Bartmann, M. Díaz Zumel, L. Ducimetière, L.M.C. Feliciano, T. Kramer, V. Namora, T. Stadlbauer, D. Standen, P. Trubacova, F.M. Velotti, C. Zannini CERN, Meyrin, Switzerland
	The SPS injection kicker magnets (MKP) were developed in the 1970’s, before beam induced power deposition was considered an issue. These magnets are very lossy from a beam impedance perspective: this is expected to be an issue during SPS operation with the higher intensity beams needed for HL-LHC. A design, with serigraphy applied to an alumina carrier, has been developed to significantly reduce the broadband beam coupling impedance and hence mitigate the heating issues. During high voltage pulse testing there were electrical discharges associated with the serigraphy. Detailed mathematical models have been developed to aid in understanding the transiently induced voltages and to reduce the magnitude and duration of electric field. In this paper, we discuss the solutions implemented to mitigate the electrical discharges while maintaining an adequately low beam-coupling impedance. In addition, the results of high voltage tests are reported. The alumina substrate has a high secondary electron yield and thus electron-cloud could be an issue, with SPS beam, if mitigating measures were not taken: this paper also discusses the measures implemented.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-THPOTK043
About •	Received ※ 07 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 13 June 2022 — Issue date ※ 17 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPOTK045	Branch Module for an Inductive Voltage Adder for Driving Kicker Magnets with a Short Circuit Termination	2875
	J. Ruf, M.J. Barnes, Y. Dutheil, T. Kramer CERN, Meyrin, Switzerland M. Sack KIT, Karlsruhe, Germany
	For driving kicker magnets terminated in a short circuit, a branch module for an inductive voltage adder has been designed and assembled. The module has been designed for a maximum charging voltage of 1.2 kV and an output current of 200 A considering the current doubling due to the short circuit termination. It features three consecutive modes of operation: energy injection, freewheeling, and energy extraction. Therefore, the topology of the branch module consists of two independently controlled SiC MOSFET switches and one diode switch. In order not to extend the field rise time of the kicker magnet significantly beyond the magnet fill time, the pulse must have a fast rise time. Hence, the switch for energy injection is driven by a gate boosting driver featuring a half bridge of GaN HEMTs and a driving voltage of 80 V. Measurements of the drain source voltage of this switch showed a fall time of 2.7 ns at a voltage of 600 V resulting in a voltage rise time of 5.4 ns at the output terminated with a resistive load. To meet both the rise time and current requirements, a parallel configuration of four SiC MOSFETs was implemented.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-THPOTK045
About •	Received ※ 16 May 2022 — Accepted ※ 14 June 2022 — Issue date ※ 10 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Fortune Telling or Physics Prediction? Deep Learning for On-Line Kicker Temperature Forecasting

957

F.M. Velotti, M.J. Barnes, B. Goddard, I. Revuelta
CERN, Meyrin, Switzerland

The injection kicker system MKP of the Super Proton Synchrotron SPS at CERN is composed of 4 kicker tanks. The MKP-L tank provides additional kick needed to inject 26 GeV Large Hadron Collider LHC 25 ns type beams. This device has been a limiting factor for operation with high intensity, due to the magnet’s broadband beam coupling impedance and consequent beam induced heating. To optimise the usage of the SPS and avoid idle (kicker cooling) time, studies were conducted to develop a recurrent deep learning model that could predict the measured temperature evolution of the MKP-L, using the beam conditions and temperature history as input. In a second stage, the ferrite temperature is also estimated putting together the external temperature predictions from accurate thermo-mechanical simulations of the kicker magnet. In this paper, the methodology is described and details of the neural network architecture used, together with the implementation of an ad-hoc loss function, are given. The results applied to the SPS 2021 operational data are presented.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST044

About •

Received ※ 06 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 18 June 2022

Cite •

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

TUPOST045

Overview of the Machine Learning and Numerical Optimiser Applications on Beam Transfer Systems for LHC and Its Injectors

961

F.M. Velotti, M.J. Barnes, E. Carlier, Y. Dutheil, M.A. Fraser, B. Goddard, N. Magnin, R.L. Ramjiawan, E. Renner, P. Van Trappen
CERN, Meyrin, Switzerland
E. Waagaard
Uppsala University, Uppsala, Sweden

Machine learning and numerical optimisation algorithms are getting more and more popular in the accelerator physics community and, thanks to the computing power available, their application in daily operation more likely. In the CERN accelerator complex, and specifically on the beam transfer systems, many promising exploitation of these numerical tools have been put in place in the last years. Some of the state-of-the-art machine learning models have been explored and used to solve problems that were never fully addressed in the past. In this paper, the most recent results of application of machine learning and numerical optimisation for injection, extraction and transfer of beam from machine and to experimental areas are presented. An overview of the possible next steps and shortcomings is finally discussed.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST045

About •

Received ※ 06 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 10 July 2022

Cite •

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

WEIYGD1

Achievements and Performance Prospects of the Upgraded LHC Injectors

1610

V. Kain, S.C.P. Albright, R. Alemany-Fernández, M.E. Angoletta, F. Antoniou, T. Argyropoulos, F. Asvesta, B. Balhan, M.J. Barnes, D. Barrientos, H. Bartosik, P. Baudrenghien, G. Bellodi, N. Biancacci, A. Boccardi, J.C.C.M. Borburgh, C. Bracco, E. Carlier, D.G. Cotte, J. Coupard, H. Damerau, G.P. Di Giovanni, A. Findlay, M.A. Fraser, A. Funken, B. Goddard, G. Hagmann, K. Hanke, A. Huschauer, M. Jaussi, I. Karpov, T. Koevener, D. Küchler, J.-B. Lallement, A. Lasheen, T.E. Levens, K.S.B. Li, A.M. Lombardi, N. Madysa, E. Mahner, M. Meddahi, L. Mether, B. Mikulec, J.C. Molendijk, E. Montesinos, D. Nisbet, F.-X. Nuiry, G. Papotti, K. Paraschou, F. Pedrosa, T. Prebibaj, S. Prodon, D. Quartullo, E. Renner, F. Roncarolo, G. Rumolo, B. Salvant, M. Schenk, R. Scrivens, E.N. Shaposhnikova, P.K. Skowroński, A. Spierer, F. Tecker, D. Valuch, F.M. Velotti, R. Wegner, C. Zannini
CERN, Meyrin, Switzerland

To provide HL-LHC performance, the CERN LHC injector chain underwent a major upgrade during an almost 2-year-long shutdown. In the first half of 2021 the injectors were gradually re-started with the aim to reach at least pre-shutdown parameters for LHC as well as for fixed target beams. The strategy of the commissioning across the complex, a summary of the many challenges and finally the achievements will be presented. Several lessons were learned and have been integrated to define the strategy for the performance ramp-up over the coming years. Remaining limitations and prospects for LHC beam parameters at the exit of the LHC injector chain in the years to come will be discussed. Finally, the emerging need for improved operability of the CERN complex will be addressed, with a description of the first efforts to meet the availability and flexibility requirements of the HL-LHC era while at the same time maximizing fixed target physics output.

Slides WEIYGD1 [5.905 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEIYGD1

About •

Received ※ 08 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 09 July 2022

Cite •

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

THPOTK043

Mitigation of High Voltage Breakdown of the Beam Screen of a CERN SPS Injection Kicker Magnet

2868

M.J. Barnes, W. Bartmann, M. Díaz Zumel, L. Ducimetière, L.M.C. Feliciano, T. Kramer, V. Namora, T. Stadlbauer, D. Standen, P. Trubacova, F.M. Velotti, C. Zannini
CERN, Meyrin, Switzerland

The SPS injection kicker magnets (MKP) were developed in the 1970’s, before beam induced power deposition was considered an issue. These magnets are very lossy from a beam impedance perspective: this is expected to be an issue during SPS operation with the higher intensity beams needed for HL-LHC. A design, with serigraphy applied to an alumina carrier, has been developed to significantly reduce the broadband beam coupling impedance and hence mitigate the heating issues. During high voltage pulse testing there were electrical discharges associated with the serigraphy. Detailed mathematical models have been developed to aid in understanding the transiently induced voltages and to reduce the magnitude and duration of electric field. In this paper, we discuss the solutions implemented to mitigate the electrical discharges while maintaining an adequately low beam-coupling impedance. In addition, the results of high voltage tests are reported. The alumina substrate has a high secondary electron yield and thus electron-cloud could be an issue, with SPS beam, if mitigating measures were not taken: this paper also discusses the measures implemented.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-THPOTK043

About •

Received ※ 07 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 13 June 2022 — Issue date ※ 17 June 2022

Cite •

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

THPOTK045

Branch Module for an Inductive Voltage Adder for Driving Kicker Magnets with a Short Circuit Termination

2875

J. Ruf, M.J. Barnes, Y. Dutheil, T. Kramer
CERN, Meyrin, Switzerland
M. Sack
KIT, Karlsruhe, Germany

For driving kicker magnets terminated in a short circuit, a branch module for an inductive voltage adder has been designed and assembled. The module has been designed for a maximum charging voltage of 1.2 kV and an output current of 200 A considering the current doubling due to the short circuit termination. It features three consecutive modes of operation: energy injection, freewheeling, and energy extraction. Therefore, the topology of the branch module consists of two independently controlled SiC MOSFET switches and one diode switch. In order not to extend the field rise time of the kicker magnet significantly beyond the magnet fill time, the pulse must have a fast rise time. Hence, the switch for energy injection is driven by a gate boosting driver featuring a half bridge of GaN HEMTs and a driving voltage of 80 V. Measurements of the drain source voltage of this switch showed a fall time of 2.7 ns at a voltage of 600 V resulting in a voltage rise time of 5.4 ns at the output terminated with a resistive load. To meet both the rise time and current requirements, a parallel configuration of four SiC MOSFETs was implemented.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-THPOTK045

About •

Received ※ 16 May 2022 — Accepted ※ 14 June 2022 — Issue date ※ 10 July 2022

Cite •

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