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Active deep learning for nonlinear optics design of a vertical FFA accelerator

Oeftiger, Adrian; Santamaria Garcia, Andrea ORCID iD icon 1,2; Lagrange, Jean-Baptiste; Hirlaender, Simon; Assmann, Ralph [Hrsg.]; McIntosh, Peter [Hrsg.]; Fabris, Alessandro [Hrsg.]; Bisoffi, Giovanni [Hrsg.]; Andrian, Ivan [Hrsg.]; Vinicola, Giulia [Hrsg.]
1 Laboratorium für Applikationen der Synchrotronstrahlung (LAS), Karlsruher Institut für Technologie (KIT)
2 Institut für Beschleunigerphysik und Technologie (IBPT), Karlsruher Institut für Technologie (KIT)

Abstract:

Vertical Fixed-Field Alternating Gradient (vFFA) accelerators exhibit particle orbits which move vertically during acceleration. This recently rediscovered circular accelerator type has several advantages over conventional ring accelerators, such as zero momentum compaction factor. At the same time, inherently non-planar orbits and a unique transverse coupling make controlling the beam dynamics a complex task. In general, betatron tune adjustment is crucial to avoid resonances, particularly when space charge effects are present. Due to highly nonlinear magnetic fields in the vFFA, it remains a challenging task to determine an optimal lattice design in terms of maximising the dynamic aperture.
This contribution describes a deep learning based algorithm which strongly improves on regular grid scans and random search to find an optimal lattice: a surrogate model is built iteratively from simulations with varying lattice parameters to predict the dynamic aperture. The training of the model follows an active learning paradigm, which thus considerably reduces the number of samples needed from the computationally expensive simulations.


Verlagsausgabe §
DOI: 10.5445/IR/1000163680
Veröffentlicht am 02.11.2023
Originalveröffentlichung
DOI: 10.18429/JACoW-IPAC2023-WEPA026
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Beschleunigerphysik und Technologie (IBPT)
Laboratorium für Applikationen der Synchrotronstrahlung (LAS)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 26.09.2023
Sprache Englisch
Identifikator ISBN: 978-3-95450-231-8
ISSN: 2673-5490
KITopen-ID: 1000163680
HGF-Programm 54.11.11 (POF IV, LK 01) Accelerator Operation, Research and Development
Erschienen in 14th International Particle Accelerator Conference, 7th-12th MAy 2023
Veranstaltung 14th International Particle Accelerator Conference (IPAC 2023), Venedig, Italien, 07.05.2023 – 12.05.2023
Verlag JACoW Publishing
Seiten 2709-2712
Schlagwörter Accelerator Physics, mc5-beam-dynamics-and-em-fields - MC5: Beam Dynamics and EM Fields, mc5-d13-machine-learning - MC5.D13: Machine Learning
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