ICALEPCS2021 - List of Keywords (SRF)

Paper	Title	Other Keywords	Page
MOPV012	The ESRF-EBS Simulator: A Commissioning Booster	controls, storage-ring, optics, TANGO	132
	S.M. Liuzzo, L.R. Carver, J.M. Chaize, L. Farvacque, A. Götz, D. Lacoste, N. Leclercq, F. Poncet, E.T. Taurel, S.M. White ESRF, Grenoble, France
	The ESRF-Extremely Brilliant Source (ESRF-EBS)* is the first-of-a-kind fourth-generation high-energy synchrotron. After only a 20-month shutdown, scientific users were back to carry out experiments with the new source. The EBS Simulator (EBSS) played a major role in the success of the commissioning of the new storage ring. Acting as a development, sandbox and training platform, the machine simulator allowed control room applications and tools to be up and ready from day one. The EBSS can also be seen as the initial block of a storage ring digital twin. The present article provides an overview of the current status of the EBS Simulator and presents the current roadmap foreseen for its future. * J.C.Biasci et al., "A Low-Emittance Lattice for the ESRF.’ Synchrotron Radiation News 27.6 (2014)
	Poster MOPV012 [16.447 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV012
About •	Received ※ 29 September 2021 Revised ※ 18 October 2021 Accepted ※ 20 November 2021 Issue date ※ 06 February 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPV025	TangoGraphQL: A GraphQL Binding for Tango Control System Web-Based Applications	TANGO, controls, framework, synchrotron	181
	J.L. Pons ESRF, Grenoble, France
	Web-based applications have seen a huge increase in popularity in recent years, replacing standalone applications. GraphQL provides a complete and understandable description of the data exchange between client browsers and back-end servers. GraphQL is a powerful query language allowing API to evolve easily and to query only what is needed. GraphQL also offers a WebSocket based protocol which perfectly fit to the Tango event system. Lots of popular tools around GraphQL offer very convenient way to browse and query data. TangoGraphQL is a pure C++ http(s) server which exports a GraphQL binding for the Tango C++ API. TangoGraphQL also exports a GraphiQL web application which allows to have a nice interactive description of the API and to test queries. TangoGraphQL* has been designed with the aim to maximize performances of JSON data serialization, a binary transfer mode is also foreseen. https://gitlab.com/tango-controls/TangoGraphQL
	Poster MOPV025 [1.374 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV025
About •	Received ※ 08 October 2021 Revised ※ 18 October 2021 Accepted ※ 04 November 2021 Issue date ※ 17 November 2021
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUBL03	Tango Controls RFCs	TANGO, controls, CORBA, software	317
	P.P. Goryl, M. Liszcz S2Innovation, Kraków, Poland S. Blanch-Torné ALBA-CELLS Synchrotron, Cerdanyola del Vallès, Spain R. Bourtembourg, A. Götz ESRF, Grenoble, France V. Hardion MAX IV Laboratory, Lund University, Lund, Sweden L. Pivetta Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
	In 2019, the Tango Controls Collaboration decided to write down a formal specification of the existing Tango Controls protocol as Requests For Comments (RFC). The work resulted in a Markdown-formatted specification rendered in HTML and PDF on Readthedocs.io. The specification is already used as a reference during Tango Controls source code maintenance and for prototyping a new implementation. All collaborating institutes and several companies were involved in the work. In addition to providing the reference, the effort brought the Community more value: review and clarification of concepts and their implementation in the core libraries in C++, Java and Python. This paper summarizes the results, provides technical and organizational details about writing the RFCs for the existing protocol and presents the impact and benefits on future maintenance and development of Tango Controls.
	Slides TUBL03 [0.743 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUBL03
About •	Received ※ 10 October 2021 Revised ※ 20 October 2021 Accepted ※ 22 December 2021 Issue date ※ 02 February 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEAR01	The Tango Controls Collaboration Status in 2021	TANGO, controls, status, experiment	544
	A. Götz, R. Bourtembourg, D. Lacoste, N. Leclercq ESRF, Grenoble, France G. Abeillé SOLEIL, Gif-sur-Yvette, France B. Bertrand, V. Hardion MAX IV Laboratory, Lund University, Lund, Sweden G. Brandl MLZ, Garching, Germany T. Braun byte physics e.K., Berlin, Germany P.P. Goryl, M. Liszcz S2Innovation, Kraków, Poland A.F. Joubert, A.J. Venter SARAO, Cape Town, South Africa C. Pascual-Izarra, S. Rubio-Manrique ALBA-CELLS Synchrotron, Cerdanyola del Vallès, Spain L. Pivetta Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
	The Tango Controls collaboration has continued to grow since ICALEPCS 2019. Multiple new releases were made of the stable release V9. The new versions include support for new compiler versions, new features and bug fixes. The collaboration has adopted a sustainable approach to kernel development to cope with changes in the community. New projects have adopted Tango Controls while others have completed commissioning of challenging new facilities. This paper will present the status of the Tango-Controls collaboration since 2019 and how it is helping new and old sites to maintain a modern control system.
	Slides WEAR01 [3.240 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEAR01
About •	Received ※ 10 October 2021 Revised ※ 15 October 2021 Accepted ※ 23 December 2021 Issue date ※ 25 February 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPV025	Initial Studies of Cavity Fault Prediction at Jefferson Laboratory	cavity, cryomodule, electron, data-acquisition	700
	L.S. Vidyaratne, A. Carpenter, R. Suleiman, C. Tennant, D.L. Turner JLab, Newport News, Virginia, USA K.M. Iftekharuddin, M. Rahman ODU, Norfolk, Virginia, USA
	Funding: This work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-06OR23177. The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data
	Poster WEPV025 [1.111 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV025
About •	Received ※ 08 October 2021 Revised ※ 19 October 2021 Accepted ※ 11 February 2022 Issue date ※ 05 March 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPV012

The ESRF-EBS Simulator: A Commissioning Booster

controls, storage-ring, optics, TANGO

132

S.M. Liuzzo, L.R. Carver, J.M. Chaize, L. Farvacque, A. Götz, D. Lacoste, N. Leclercq, F. Poncet, E.T. Taurel, S.M. White
ESRF, Grenoble, France

The ESRF-Extremely Brilliant Source (ESRF-EBS)* is the first-of-a-kind fourth-generation high-energy synchrotron. After only a 20-month shutdown, scientific users were back to carry out experiments with the new source. The EBS Simulator (EBSS) played a major role in the success of the commissioning of the new storage ring. Acting as a development, sandbox and training platform, the machine simulator allowed control room applications and tools to be up and ready from day one. The EBSS can also be seen as the initial block of a storage ring digital twin. The present article provides an overview of the current status of the EBS Simulator and presents the current roadmap foreseen for its future.
* J.C.Biasci et al., "A Low-Emittance Lattice for the ESRF.’ Synchrotron Radiation News 27.6 (2014)

Poster MOPV012 [16.447 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV012

About •

Received ※ 29 September 2021 Revised ※ 18 October 2021 Accepted ※ 20 November 2021 Issue date ※ 06 February 2022

Cite •

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

MOPV025

TangoGraphQL: A GraphQL Binding for Tango Control System Web-Based Applications

TANGO, controls, framework, synchrotron

181

J.L. Pons
ESRF, Grenoble, France

Web-based applications have seen a huge increase in popularity in recent years, replacing standalone applications. GraphQL provides a complete and understandable description of the data exchange between client browsers and back-end servers. GraphQL is a powerful query language allowing API to evolve easily and to query only what is needed. GraphQL also offers a WebSocket based protocol which perfectly fit to the Tango event system. Lots of popular tools around GraphQL offer very convenient way to browse and query data. TangoGraphQL is a pure C++ http(s) server which exports a GraphQL binding for the Tango C++ API. TangoGraphQL also exports a GraphiQL web application which allows to have a nice interactive description of the API and to test queries. TangoGraphQL* has been designed with the aim to maximize performances of JSON data serialization, a binary transfer mode is also foreseen.
https://gitlab.com/tango-controls/TangoGraphQL

Poster MOPV025 [1.374 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV025

About •

Received ※ 08 October 2021 Revised ※ 18 October 2021 Accepted ※ 04 November 2021 Issue date ※ 17 November 2021

Cite •

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

TUBL03

Tango Controls RFCs

TANGO, controls, CORBA, software

317

P.P. Goryl, M. Liszcz
S2Innovation, Kraków, Poland
S. Blanch-Torné
ALBA-CELLS Synchrotron, Cerdanyola del Vallès, Spain
R. Bourtembourg, A. Götz
ESRF, Grenoble, France
V. Hardion
MAX IV Laboratory, Lund University, Lund, Sweden
L. Pivetta
Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy

In 2019, the Tango Controls Collaboration decided to write down a formal specification of the existing Tango Controls protocol as Requests For Comments (RFC). The work resulted in a Markdown-formatted specification rendered in HTML and PDF on Readthedocs.io. The specification is already used as a reference during Tango Controls source code maintenance and for prototyping a new implementation. All collaborating institutes and several companies were involved in the work. In addition to providing the reference, the effort brought the Community more value: review and clarification of concepts and their implementation in the core libraries in C++, Java and Python. This paper summarizes the results, provides technical and organizational details about writing the RFCs for the existing protocol and presents the impact and benefits on future maintenance and development of Tango Controls.

Slides TUBL03 [0.743 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUBL03

About •

Received ※ 10 October 2021 Revised ※ 20 October 2021 Accepted ※ 22 December 2021 Issue date ※ 02 February 2022

Cite •

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

WEAR01

The Tango Controls Collaboration Status in 2021

TANGO, controls, status, experiment

544

A. Götz, R. Bourtembourg, D. Lacoste, N. Leclercq
ESRF, Grenoble, France
G. Abeillé
SOLEIL, Gif-sur-Yvette, France
B. Bertrand, V. Hardion
MAX IV Laboratory, Lund University, Lund, Sweden
G. Brandl
MLZ, Garching, Germany
T. Braun
byte physics e.K., Berlin, Germany
P.P. Goryl, M. Liszcz
S2Innovation, Kraków, Poland
A.F. Joubert, A.J. Venter
SARAO, Cape Town, South Africa
C. Pascual-Izarra, S. Rubio-Manrique
ALBA-CELLS Synchrotron, Cerdanyola del Vallès, Spain
L. Pivetta
Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy

The Tango Controls collaboration has continued to grow since ICALEPCS 2019. Multiple new releases were made of the stable release V9. The new versions include support for new compiler versions, new features and bug fixes. The collaboration has adopted a sustainable approach to kernel development to cope with changes in the community. New projects have adopted Tango Controls while others have completed commissioning of challenging new facilities. This paper will present the status of the Tango-Controls collaboration since 2019 and how it is helping new and old sites to maintain a modern control system.

Slides WEAR01 [3.240 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEAR01

About •

Received ※ 10 October 2021 Revised ※ 15 October 2021 Accepted ※ 23 December 2021 Issue date ※ 25 February 2022

Cite •

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

WEPV025

Initial Studies of Cavity Fault Prediction at Jefferson Laboratory

cavity, cryomodule, electron, data-acquisition

700

L.S. Vidyaratne, A. Carpenter, R. Suleiman, C. Tennant, D.L. Turner
JLab, Newport News, Virginia, USA
K.M. Iftekharuddin, M. Rahman
ODU, Norfolk, Virginia, USA

Funding: This work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-06OR23177.
The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data

Poster WEPV025 [1.111 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV025

About •

Received ※ 08 October 2021 Revised ※ 19 October 2021 Accepted ※ 11 February 2022 Issue date ※ 05 March 2022

Cite •

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