ICALEPCS2019 - List of Keywords (proton)

Paper	Title	Other Keywords	Page
MOCPL05	Software Framework QAClient for Measurement/Automation In Proton Therapy Centers	controls, LabView, database, framework	86
	A. Mayor, O. Actis, D. Meer, B. Rohrer PSI, Villigen PSI, Switzerland
	PSI operates a proton center for cancer treatments consisting of treatment areas Gantry 2, Gantry 3 and OPTIS2. For calibration measurements and quality assurance procedures which have to be executed on a frequent basis and involve different systems and software products, a software framework (QAClient) was developed at PSI. QAClient provides a configurable and extensible framework communicating with PSI control systems, measurement devices, databases and commercial products as LabVIEW and MATLAB. It supports automation of test protocols with user interaction, data analysis and data storage as well as generating of reports. It runs on Java and on different operating system platforms and offers an intuitive graphical user interface. It is used for clinical checks, calibration and tuning measurements, system integration tests and patient table calibrations. New tasks can be configured using standard tasks, without programming effort. QAClient is used for Gantry 2 Daily Check which reduces the execution time by 70% and simplifies measurements so less trained staff can execute it. QA reports are generated automatically and data gets archived and can be used for trend analysis.
	Slides MOCPL05 [2.453 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOCPL05
About •	paper received ※ 27 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOMPR006	Performance of the ALICE Luminosity Leveling Software Architecture in the Pb-Pb Physics Run	luminosity, operation, software, experiment	167
	G. Valentino University of Malta, Information and Communication Technology, Msida, Malta G. De Cataldo, M. Hostettler CERN, Geneva, Switzerland A. Franco INFN-Bari, Bari, Italy O.B. Visnyei KFKI, Budapest, Hungary
	Luminosity leveling is performed in the ALICE experiment of the Large Hadron Collider (LHC) in order to limit the event pile-up probability, and ensure a safe operation for the detectors. It will be even more important during Run 3 when 50 KHz Pb ion-Pb ion (Pb-Pb) collisions will be delivered in IP2. On the ALICE side, it is handled by the ALICE-LHC Interface project, which also ensures an online data exchange between ALICE and the LHC. An automated luminosity leveling algorithm was developed for the proton-proton physics run, and was also deployed for the Pb-Pb run with some minor changes following experience gained. The algorithm is implemented in the SIMATIC WinCC SCADA environment, and determines the leveling step from measured beam parameters received from the LHC, and the luminosity recorded by ALICE. In this paper, the software architecture of the luminosity leveling software is presented, and the performance achieved during the Pb-Pb run and Van der Meer scans is discussed.
	Poster MOMPR006 [3.292 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOMPR006
About •	paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPHA090	Design of Vessel and Beamline Vacuum and Gas Control System for Proton Radiography	controls, vacuum, network, software	417
	P.S. Marroquin, J.D. Bernardin, J.G. Gioia, D.A. Hathcoat, A. Llobet, H.J. Sandin, W. Winton LANL, Los Alamos, New Mexico, USA
	Funding: Supported by the US Department of Energy, Los Alamos National Laboratory. Managed by Triad National Security, LLC, for the DOE National Nuclear Security Administration (Contract 89233218CNA000001). A new capability for conducting explosively-driven dynamic physics experiments at the Proton Radiographic (pRad) facility at Los Alamos National Laboratory (LANL) is in development. The pRad facility, an experimental area of the Los Alamos Neutron Science Center (LANSCE), performs multi frame proton radiography of materials subjected to an explosive process. Under design is a new beamline with confinement and containment vessels and required supporting systems and components. Five distinct vacuum sections have been identified, each equipped with complete vacuum pumping assemblies. Inert gas systems are included for backfill and pressurization and supporting piping integrates the subsystems for gas distribution and venting. This paper will discuss the design of the independent vacuum control subsystems, the integrated vacuum and gas control system and full incorporation into the Experimental Physics and Industrial Control System (EPICS) based LANSCE Control Systems and Networks. LA-UR-19-23843
	Poster MOPHA090 [2.167 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA090
About •	paper received ※ 27 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPHA138	Beam Gate Control System for the Proton Injector and Beamlines on KOMAC	timing, controls, operation, linac	551
	Y.G. Song, H.S. Kim, J.H. Kim, H.-J. Kwon Korea Atomic Energy Research Institute (KAERI), Gyeongbuk, Republic of Korea
	Funding: This work has been supported through KOMAC (Korea Multi-purpose Accelerator Complex) operation fund of KAERI by MSIT (Ministry of Science and ICT). The Korea Multi-purpose Accelerator Complex (KOMAC) 100 MeV proton linac operates with the timing system to change real-time timing parameters for low and high-flux proton beam utilization. The main requirements are to synchronize the operation of the facility including linac, target, and diagnostics, to provide a variable beam repetition rate up to 60 Hz, and to support post-mortem analysis when a beam trip occurs. The timing system, which consists of one event generator and eleven event receivers, is configured to control the beam gate and beam sequence to distribute the proton beam to the beam line. Corresponding to user’s demands, beam gate should be controlled, and the beam distribution must be precisely synchronized with the main reference signal. The timing system is configured with sequence logic for beam gate control, and the timing events can trigger the software to perform actions including beam on or off, post-mortem data acquisition, and beam distribution on the beam lines. The results of the timing control system for the beam gate and beam distribution are presented.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA138
About •	paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEMPL001	An Application of Machine Learning for the Analysis of Temperature Rise on the Production Target in Hadron Experimental Facility at J-PARC	target, operation, EPICS, extraction	992
	K. Agari, H. Akiyama, Y. Morino, Y. Sato, A. Toyoda KEK, Tsukuba, Japan
	Hadron Experimental Facility (HEF) is designed to handle an intense slow-extraction proton beam from the 30 GeV Main Ring (MR) of Japan Proton Accelerator Research Complex (J-PARC). Proton beams of 5·10¹³ protons per spill during 2 seconds in the 5.2 seconds accelerator operating cycle were extracted from MR to HEF in the 2018 run. In order to evaluate soundness of the target, we have analyzed variation of temperature rise on the production target, which depends on the beam conditions on the target. Predicted temperature rise is calculated from the existing data of the beam intensity, the spill length (duration of the beam extraction) and the beam position on the target, using a linear regression analysis with a machine learning library, Scikit-learn. As a result, the predicted temperature rise on the production target shows good agreement with the measured one. We have also examined whether the present method of the predicted temperature rise from the existing data can be applied to unknown data in the future runs. The present paper reports the status of the measurement system of temperature rise on the target with machine learning in detail.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEMPL001
About •	paper received ※ 28 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPHA127	The IRRAD Proton Irradiation Facility Control, Data Management and Beam Diagnostic Systems: An Outlook of the Major Upgrades Beyond the CERN Long Shutdown 2	radiation, controls, experiment, operation	1389
	F. Ravotti, B. Gkotse, M. Glaser, I.M. Mateu, V. Meskova, G. Pezzullo CERN, Geneva, Switzerland B. Gkotse, P. Jouvelot MINES ParisTech, PSL Research University, Paris, France J.M. Sallese EPFL, Lausanne, Switzerland
	Funding: This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under Grant Agreement no. 654168. The IRRAD proton irradiation facility at CERN was built during the Long Shutdown 1 (LS1) to address the irradiation experiment needs of the community working for the High-Luminosity (HL) upgrade of the LHC. The present IRRAD is an upgrade of a historical service at CERN that, since the 90’s, exploits the high-intensity 24 GeV/c PS proton beam for radiation-hardness studies of detector, accelerator and semiconductor components and materials. During its first run (2015-2018), IRRAD provided a key service to the CERN community, with more than 2500 samples irradiated. IRRAD is operated via custom-made irradiation systems, beam diagnostics and data management tools. During the Long Shutdown 2 (LS2), IRRAD will undergo several upgrades in order to cope also with new requirements arising for projects beyond the HL-LHC. In this paper, we (1) describe the various hardware and software equipment developed for IRRAD, and (2) present the main challenges encountered during the first years of operation, which have driven most of the improvements planned for LS2 such as applying machine-learning techniques in the processing and real-time analysis of beam profile data.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA127
About •	paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THCPL06	Introducing Big Data Analysis in a Proton Therapy Facility to Reduce Technical Downtime	controls, interlocks, status, EPICS	1608
	P. Fernandez Carmona, Z. Chowdhuri, S.G. Ebner, F. Gagnon-Moisan, M. Grossmann, J. Snuverink, D.C. Weber PSI, Villigen PSI, Switzerland
	At the center for Proton Therapy of the Paul Scherrer Institute about 450 cancer patients are treated yearly using accelerated protons in three treatment areas. The facility is active since 1984 and for each patient we keep detailed log files containing machine measurements during each fraction of the treatment, which we analyze daily to guarantee dose and position values within the prescribed tolerances. Furthermore, each control and safety system generates textual log files as well as periodic measurements such as pressure, temperature, beam intensity, magnetic fields or reaction time of components. This adds up currently to approximately 5 GB per day. Downtime of the facility is both inconvenient for patients and staff, as well as financially relevant. This article describes how we have extended our data analysis strategies using machine archived parameters and online measurements to understand interdependencies, to perform preventive maintenance of ageing components and to optimize processes. We have chosen Python to interface, structure and analyze the different data sources in an standardized manner. The online channels have been accessed via an EPICS archiver.
	Slides THCPL06 [7.028 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-THCPL06
About •	paper received ※ 30 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOCPL05

Software Framework QAClient for Measurement/Automation In Proton Therapy Centers

controls, LabView, database, framework

86

A. Mayor, O. Actis, D. Meer, B. Rohrer
PSI, Villigen PSI, Switzerland

PSI operates a proton center for cancer treatments consisting of treatment areas Gantry 2, Gantry 3 and OPTIS2. For calibration measurements and quality assurance procedures which have to be executed on a frequent basis and involve different systems and software products, a software framework (QAClient) was developed at PSI. QAClient provides a configurable and extensible framework communicating with PSI control systems, measurement devices, databases and commercial products as LabVIEW and MATLAB. It supports automation of test protocols with user interaction, data analysis and data storage as well as generating of reports. It runs on Java and on different operating system platforms and offers an intuitive graphical user interface. It is used for clinical checks, calibration and tuning measurements, system integration tests and patient table calibrations. New tasks can be configured using standard tasks, without programming effort. QAClient is used for Gantry 2 Daily Check which reduces the execution time by 70% and simplifies measurements so less trained staff can execute it. QA reports are generated automatically and data gets archived and can be used for trend analysis.

Slides MOCPL05 [2.453 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOCPL05

About •

paper received ※ 27 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

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

MOMPR006

Performance of the ALICE Luminosity Leveling Software Architecture in the Pb-Pb Physics Run

luminosity, operation, software, experiment

167

G. Valentino
University of Malta, Information and Communication Technology, Msida, Malta
G. De Cataldo, M. Hostettler
CERN, Geneva, Switzerland
A. Franco
INFN-Bari, Bari, Italy
O.B. Visnyei
KFKI, Budapest, Hungary

Luminosity leveling is performed in the ALICE experiment of the Large Hadron Collider (LHC) in order to limit the event pile-up probability, and ensure a safe operation for the detectors. It will be even more important during Run 3 when 50 KHz Pb ion-Pb ion (Pb-Pb) collisions will be delivered in IP2. On the ALICE side, it is handled by the ALICE-LHC Interface project, which also ensures an online data exchange between ALICE and the LHC. An automated luminosity leveling algorithm was developed for the proton-proton physics run, and was also deployed for the Pb-Pb run with some minor changes following experience gained. The algorithm is implemented in the SIMATIC WinCC SCADA environment, and determines the leveling step from measured beam parameters received from the LHC, and the luminosity recorded by ALICE. In this paper, the software architecture of the luminosity leveling software is presented, and the performance achieved during the Pb-Pb run and Van der Meer scans is discussed.

Poster MOMPR006 [3.292 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOMPR006

About •

paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

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

MOPHA090

Design of Vessel and Beamline Vacuum and Gas Control System for Proton Radiography

controls, vacuum, network, software

417

P.S. Marroquin, J.D. Bernardin, J.G. Gioia, D.A. Hathcoat, A. Llobet, H.J. Sandin, W. Winton
LANL, Los Alamos, New Mexico, USA

Funding: Supported by the US Department of Energy, Los Alamos National Laboratory. Managed by Triad National Security, LLC, for the DOE National Nuclear Security Administration (Contract 89233218CNA000001).
A new capability for conducting explosively-driven dynamic physics experiments at the Proton Radiographic (pRad) facility at Los Alamos National Laboratory (LANL) is in development. The pRad facility, an experimental area of the Los Alamos Neutron Science Center (LANSCE), performs multi frame proton radiography of materials subjected to an explosive process. Under design is a new beamline with confinement and containment vessels and required supporting systems and components. Five distinct vacuum sections have been identified, each equipped with complete vacuum pumping assemblies. Inert gas systems are included for backfill and pressurization and supporting piping integrates the subsystems for gas distribution and venting. This paper will discuss the design of the independent vacuum control subsystems, the integrated vacuum and gas control system and full incorporation into the Experimental Physics and Industrial Control System (EPICS) based LANSCE Control Systems and Networks.
LA-UR-19-23843

Poster MOPHA090 [2.167 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA090

About •

paper received ※ 27 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

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

MOPHA138

Beam Gate Control System for the Proton Injector and Beamlines on KOMAC

timing, controls, operation, linac

551

Y.G. Song, H.S. Kim, J.H. Kim, H.-J. Kwon
Korea Atomic Energy Research Institute (KAERI), Gyeongbuk, Republic of Korea

Funding: This work has been supported through KOMAC (Korea Multi-purpose Accelerator Complex) operation fund of KAERI by MSIT (Ministry of Science and ICT).
The Korea Multi-purpose Accelerator Complex (KOMAC) 100 MeV proton linac operates with the timing system to change real-time timing parameters for low and high-flux proton beam utilization. The main requirements are to synchronize the operation of the facility including linac, target, and diagnostics, to provide a variable beam repetition rate up to 60 Hz, and to support post-mortem analysis when a beam trip occurs. The timing system, which consists of one event generator and eleven event receivers, is configured to control the beam gate and beam sequence to distribute the proton beam to the beam line. Corresponding to user’s demands, beam gate should be controlled, and the beam distribution must be precisely synchronized with the main reference signal. The timing system is configured with sequence logic for beam gate control, and the timing events can trigger the software to perform actions including beam on or off, post-mortem data acquisition, and beam distribution on the beam lines. The results of the timing control system for the beam gate and beam distribution are presented.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA138

About •

paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

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

WEMPL001

An Application of Machine Learning for the Analysis of Temperature Rise on the Production Target in Hadron Experimental Facility at J-PARC

target, operation, EPICS, extraction

992

K. Agari, H. Akiyama, Y. Morino, Y. Sato, A. Toyoda
KEK, Tsukuba, Japan

Hadron Experimental Facility (HEF) is designed to handle an intense slow-extraction proton beam from the 30 GeV Main Ring (MR) of Japan Proton Accelerator Research Complex (J-PARC). Proton beams of 5·10¹³ protons per spill during 2 seconds in the 5.2 seconds accelerator operating cycle were extracted from MR to HEF in the 2018 run. In order to evaluate soundness of the target, we have analyzed variation of temperature rise on the production target, which depends on the beam conditions on the target. Predicted temperature rise is calculated from the existing data of the beam intensity, the spill length (duration of the beam extraction) and the beam position on the target, using a linear regression analysis with a machine learning library, Scikit-learn. As a result, the predicted temperature rise on the production target shows good agreement with the measured one. We have also examined whether the present method of the predicted temperature rise from the existing data can be applied to unknown data in the future runs. The present paper reports the status of the measurement system of temperature rise on the target with machine learning in detail.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEMPL001

About •

paper received ※ 28 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

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

WEPHA127

The IRRAD Proton Irradiation Facility Control, Data Management and Beam Diagnostic Systems: An Outlook of the Major Upgrades Beyond the CERN Long Shutdown 2

radiation, controls, experiment, operation

1389

F. Ravotti, B. Gkotse, M. Glaser, I.M. Mateu, V. Meskova, G. Pezzullo
CERN, Geneva, Switzerland
B. Gkotse, P. Jouvelot
MINES ParisTech, PSL Research University, Paris, France
J.M. Sallese
EPFL, Lausanne, Switzerland

Funding: This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under Grant Agreement no. 654168.
The IRRAD proton irradiation facility at CERN was built during the Long Shutdown 1 (LS1) to address the irradiation experiment needs of the community working for the High-Luminosity (HL) upgrade of the LHC. The present IRRAD is an upgrade of a historical service at CERN that, since the 90’s, exploits the high-intensity 24 GeV/c PS proton beam for radiation-hardness studies of detector, accelerator and semiconductor components and materials. During its first run (2015-2018), IRRAD provided a key service to the CERN community, with more than 2500 samples irradiated. IRRAD is operated via custom-made irradiation systems, beam diagnostics and data management tools. During the Long Shutdown 2 (LS2), IRRAD will undergo several upgrades in order to cope also with new requirements arising for projects beyond the HL-LHC. In this paper, we (1) describe the various hardware and software equipment developed for IRRAD, and (2) present the main challenges encountered during the first years of operation, which have driven most of the improvements planned for LS2 such as applying machine-learning techniques in the processing and real-time analysis of beam profile data.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA127

About •

paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

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

THCPL06

Introducing Big Data Analysis in a Proton Therapy Facility to Reduce Technical Downtime

controls, interlocks, status, EPICS

1608

P. Fernandez Carmona, Z. Chowdhuri, S.G. Ebner, F. Gagnon-Moisan, M. Grossmann, J. Snuverink, D.C. Weber
PSI, Villigen PSI, Switzerland

At the center for Proton Therapy of the Paul Scherrer Institute about 450 cancer patients are treated yearly using accelerated protons in three treatment areas. The facility is active since 1984 and for each patient we keep detailed log files containing machine measurements during each fraction of the treatment, which we analyze daily to guarantee dose and position values within the prescribed tolerances. Furthermore, each control and safety system generates textual log files as well as periodic measurements such as pressure, temperature, beam intensity, magnetic fields or reaction time of components. This adds up currently to approximately 5 GB per day. Downtime of the facility is both inconvenient for patients and staff, as well as financially relevant. This article describes how we have extended our data analysis strategies using machine archived parameters and online measurements to understand interdependencies, to perform preventive maintenance of ageing components and to optimize processes. We have chosen Python to interface, structure and analyze the different data sources in an standardized manner. The online channels have been accessed via an EPICS archiver.

Slides THCPL06 [7.028 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-THCPL06

About •

paper received ※ 30 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

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