ICALEPCS2021 - List of Authors (Eguiraun, M.)

Paper	Title	Page
MOAL01	Maturity of the MAX IV Laboratory in Operation and Phase II Development	1
	V. Hardion, P.J. Bell, M. Eguiraun, T. Eriksson, Á. Freitas, J.M. Klingberg, M. Lindberg, Z. Matej, S. Padmanabhan, A. Salnikov, P. Sjöblom, D.P. Spruce MAX IV Laboratory, Lund University, Lund, Sweden
	MAX~IV Laboratory, the first 4th generation synchrotron located in the south of Sweden, entered operation in 2017 with the first three experimental stations. In the past two years the project organisation has been focused on phase II of the MAX IV Laboratory development, aiming to raise the number of beamlines in operation to 16. The KITS group, responsible for the control and computing systems of the entire laboratory, was a major actor in the realisation of this phase as well as in the continuous up-keep of the user operation. The challenge consisted principally of establishing a clear project management plan for the support groups, including KITS, to handle this high load in an efficient and focused way, meanwhile gaining the experience of operating a 4th generation light source. The momentum gained was impacted by the last extensive shutdown due to the pandemic and shifted toward the remote user experiment, taking advantage of web technologies. This article focuses on how KITS has handled this growing phase in term of technology and organisation, to finally describe the new perspective for the MAX IV Laboratory, which will face a bright future.
	Slides MOAL01 [79.837 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOAL01
About •	Received ※ 10 October 2021 Revised ※ 22 November 2021 Accepted ※ 13 December 2021 Issue date ※ 22 December 2021
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FRAR01	Taranta, the No-Code Web Dashboard in Production	1017
	M. Eguiraun, A. Amjad, J. Forsberg, V. Hardion, Y.L. Li, L.M. Nguyen, J.T.K. Rosenqvist, M. Saad MAX IV Laboratory, Lund University, Lund, Sweden V. Alberti INAF-OAT, Trieste, Italy M. Canzari INAF - OAAB, Teramo, Italy H.R. Ribeiro Universidade do Porto, Faculdade de Ciências, Porto, Portugal
	The remote control and monitoring of accelerators and experimental setup has become an essential enabler when remote work has become the norm for the last 2 years. Unlike the desktop user interfaces which have been developed for the use of physical workstations, Web application are naturally accessible remotely via the ubiquitous web browsers. On the other hand, Web technology development need a specific knowledge which has yet to be disseminate in the control system engineering. And desktop frameworks still have the benefit of rapid and easy development even for the non-specialist. Taranta Suite is a collection of web applications jointly developed by MAX IV Laboratory and the SKA Organization, for the Tango Control System. Totally in line with the ’no-code’ trend, truly little knowledge of web technologies is needed. An operator can create a graphical user interface on-the-fly and then, can share instantly this application. Authentication and authorization ensure to give the right level access to the users. This paper will describe the system, the React and GQL implementation and the first usage at the different facilities.
	Slides FRAR01 [3.243 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-FRAR01
About •	Received ※ 10 October 2021 Revised ※ 08 November 2021 Accepted ※ 20 November 2021 Issue date ※ 11 January 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FRBR06	Automated ML-based Sample Centering for Macromolecular X-Ray Crystallography with MXAimbot
	I. Lindhé, O. Aurelius, M. Eguiraun, A. Gonzalez, E. Jagudin, G. Lima, Z. Matej, J. Nan, J. Schurmann MAX IV Laboratory, Lund University, Lund, Sweden J.W. Janneck Lund Institute of Technology (LTH), Lund University, Lund, Sweden
	MXAimbot is a neural network based tool, designed to automate the task of centering samples for macro-molecular X-ray crystallography experiments before exposing the sample to the beam. MXAimbot uses a convolutional neural network (CNN) trained on a few thousands images from an industrial vision camera pointed at the sample to predict suitable crystal centering for subsequent X-ray data collection. The motivation for this project is that the machine vision automated sample positioning allows X-ray laboratories and synchrotron beamlines to offer a more efficient alternative for the manual centering, which is time consuming and difficult to automate with conventional image analysis, and for the X-ray mesh scan centering, which can introduce radiation damage to the crystal. MXAimbot can be used to improve results of standard LUCID loop centering for fully automated data collection in fragment-screening campaigns. No need for sample rotation should be an additional advantage.
	Slides FRBR06 [12.433 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Maturity of the MAX IV Laboratory in Operation and Phase II Development

1

V. Hardion, P.J. Bell, M. Eguiraun, T. Eriksson, Á. Freitas, J.M. Klingberg, M. Lindberg, Z. Matej, S. Padmanabhan, A. Salnikov, P. Sjöblom, D.P. Spruce
MAX IV Laboratory, Lund University, Lund, Sweden

MAX~IV Laboratory, the first 4th generation synchrotron located in the south of Sweden, entered operation in 2017 with the first three experimental stations. In the past two years the project organisation has been focused on phase II of the MAX IV Laboratory development, aiming to raise the number of beamlines in operation to 16. The KITS group, responsible for the control and computing systems of the entire laboratory, was a major actor in the realisation of this phase as well as in the continuous up-keep of the user operation. The challenge consisted principally of establishing a clear project management plan for the support groups, including KITS, to handle this high load in an efficient and focused way, meanwhile gaining the experience of operating a 4th generation light source. The momentum gained was impacted by the last extensive shutdown due to the pandemic and shifted toward the remote user experiment, taking advantage of web technologies. This article focuses on how KITS has handled this growing phase in term of technology and organisation, to finally describe the new perspective for the MAX IV Laboratory, which will face a bright future.

Slides MOAL01 [79.837 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOAL01

About •

Received ※ 10 October 2021 Revised ※ 22 November 2021 Accepted ※ 13 December 2021 Issue date ※ 22 December 2021

Cite •

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

FRAR01

Taranta, the No-Code Web Dashboard in Production

1017

M. Eguiraun, A. Amjad, J. Forsberg, V. Hardion, Y.L. Li, L.M. Nguyen, J.T.K. Rosenqvist, M. Saad
MAX IV Laboratory, Lund University, Lund, Sweden
V. Alberti
INAF-OAT, Trieste, Italy
M. Canzari
INAF - OAAB, Teramo, Italy
H.R. Ribeiro
Universidade do Porto, Faculdade de Ciências, Porto, Portugal

The remote control and monitoring of accelerators and experimental setup has become an essential enabler when remote work has become the norm for the last 2 years. Unlike the desktop user interfaces which have been developed for the use of physical workstations, Web application are naturally accessible remotely via the ubiquitous web browsers. On the other hand, Web technology development need a specific knowledge which has yet to be disseminate in the control system engineering. And desktop frameworks still have the benefit of rapid and easy development even for the non-specialist. Taranta Suite is a collection of web applications jointly developed by MAX IV Laboratory and the SKA Organization, for the Tango Control System. Totally in line with the ’no-code’ trend, truly little knowledge of web technologies is needed. An operator can create a graphical user interface on-the-fly and then, can share instantly this application. Authentication and authorization ensure to give the right level access to the users. This paper will describe the system, the React and GQL implementation and the first usage at the different facilities.

Slides FRAR01 [3.243 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-FRAR01

About •

Received ※ 10 October 2021 Revised ※ 08 November 2021 Accepted ※ 20 November 2021 Issue date ※ 11 January 2022

Cite •

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

FRBR06

Automated ML-based Sample Centering for Macromolecular X-Ray Crystallography with MXAimbot

I. Lindhé, O. Aurelius, M. Eguiraun, A. Gonzalez, E. Jagudin, G. Lima, Z. Matej, J. Nan, J. Schurmann
MAX IV Laboratory, Lund University, Lund, Sweden
J.W. Janneck
Lund Institute of Technology (LTH), Lund University, Lund, Sweden

MXAimbot is a neural network based tool, designed to automate the task of centering samples for macro-molecular X-ray crystallography experiments before exposing the sample to the beam. MXAimbot uses a convolutional neural network (CNN) trained on a few thousands images from an industrial vision camera pointed at the sample to predict suitable crystal centering for subsequent X-ray data collection. The motivation for this project is that the machine vision automated sample positioning allows X-ray laboratories and synchrotron beamlines to offer a more efficient alternative for the manual centering, which is time consuming and difficult to automate with conventional image analysis, and for the X-ray mesh scan centering, which can introduce radiation damage to the crystal. MXAimbot can be used to improve results of standard LUCID loop centering for fully automated data collection in fragment-screening campaigns. No need for sample rotation should be an additional advantage.

Slides FRBR06 [12.433 MB]

Cite •

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