Paper | Title | Page |
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TUPOST055 | Toward Machine Learning-Based Adaptive Control and Global Feedback for Compact Accelerators | 991 |
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Funding: This work was supported by the DOE Office of Science Graduate Student Research (SCGSR) program, by the DOE Office of Basic Energy Sciences under Contract No. DE-AC02-05CH11231, … continued 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. … [continued from below] by the DOE Office of Science, Office of High Energy Physics under contract number 89233218CNA000001 and DE-AC02-05CH11231 and by the NSF under Grant No. PHY-1549132. |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST055 | |
About • | Received ※ 08 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 20 June 2022 | |
Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |