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RIS citation export for TUPOST055: Toward Machine Learning-Based Adaptive Control and Global Feedback for Compact Accelerators

TY  - CONF
AU  - Cropp V, F.W.
AU  - Filippetto, D.
AU  - Gilardi, A.
AU  - Musumeci, P.
AU  - Paiagua, S.
AU  - Scheinker, A.
AU  - Wang, D.
ED  - Zimmermann, Frank
ED  - Tanaka, Hitoshi
ED  - Sudmuang, Porntip
ED  - Klysubun, Prapong
ED  - Sunwong, Prapaiwan
ED  - Chanwattana, Thakonwat
ED  - Petit-Jean-Genaz, Christine
ED  - Schaa, Volker R.W.
TI  - Toward Machine Learning-Based Adaptive Control and Global Feedback for Compact Accelerators
J2  - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022
CY  - Bangkok, Thailand
T2  - International Particle Accelerator Conference
T3  - 13
LA  - english
AB  - The HiRES beamline at Lawrence Berkeley National Laboratory (USA) is a state-of-the-art compact accelerator providing ultrafast relativistic electron pulses at MHz repetition rates, for applications in ultrafast science and for particle accelerator science and technology R&D. Using HiRES as testbed, we seek to apply recent developments in machine learning and computational techniques for machine-learning-based adaptive control, and eventually, a full control system based on global feedback. The ultimate goal is to demonstrate the benefits of such a suite of controls to UED, including increased temporal and spatial resolution. Concrete steps toward these goals are presented, including automatic, model-independent tuning for accelerators, and energy virtual diagnostics with direct application to improving UED temporal resolution.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 991
EP  - 994
KW  - controls
KW  - feedback
KW  - diagnostics
KW  - electron
KW  - quadrupole
DA  - 2022/07
PY  - 2022
SN  - 2673-5490
SN  - 978-3-95450-227-1
DO  - doi:10.18429/JACoW-IPAC2022-TUPOST055
UR  - https://jacow.org/ipac2022/papers/tupost055.pdf
ER  -