The objective of the Accelerator Toolbox workshop is to bring together international scientists to exchange ideas and discuss best practices about use of the accelerator toolbox code (matlab or python) for beam dynamics in charged particle accelerators.
The workshop programme consists of plenary talks and poster presentations.
The agenda will include (but is not limited to) presentations of:
- recent upgrades of the software
- experience of use for simulations: commissioning, DA, Injection efficiency and lifetime, impact of ID gaps on optics, losses & collimation, injection, optics design, optics matching, etc...
- experience of use of AT in control room: MML, Pytac, digital twins, etc..
- use of AT for collective effects studies
- discussion on code status, maintenance and priority for future developments
Please propose topics for presentation using this survey: AT workshop SURVEY
All topics that participants would like to discuss will be addressed either as talks or as poster presentations.
An international Scientific Board will support the LOC in the selection and organization of all proposed contributions.
Students and non experts are most welcome. A session of training to use matlab/python AT with real life examples will take place if a sufficient number of people show interest.
Remote attendance at the workshop will be possible.
LOC
Simone Liuzzo (ESRF)
Simon White (ESRF)
Nicola Carmignani (ESRF)
Lee Carver (ESRF)
Thomas Perron (ESRF)
Lina Hoummi (ESRF)
Philippa Lean (ESRF)
Ilya Agapov (DESY)
Joachim Keil (DESY)
Lukas Malina (DESY)
Bianca Veglia (DESY)
Elaf Musa (DESY)
Thorsten Hellert (LBL)
Scientific Board
Laurent Nadolski (SOLEIL)
Xiaobiao Huang (SLAC)
Zeus Marti (ALBA)
Boaz Nash (RADIASOFT)
Eugene Tan (Australian Synchrotron)
Laurent Farvacque (ESRF)
Dates and Deadlines
15th June 2023: closure of AT survey for presentation proposals
30th June 2023: closure of registration
15th July 2023: detailed timetable
2-3 October 2023: Workshop
3 October 2023: matlab/python AT introductory training
The workshop has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement #871072. EURIZON Task 4.1 EU project.
