Title |
Enhancing the MOGA Optimization Process at ALS-U with Machine Learning |
Authors |
- Y. Lu, M.P. Ehrlichman, T. Hellert, S.C. Leemann, H. Nishimura, C. Sun, M. Venturini
LBNL, Berkeley, California, USA
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Abstract |
The bare lattice optimization for the linear and nonlinear ALS-U storage ring lattice, even without reverse bending, comprises 11 degrees of freedom (DoF) and is therefore a very complex and highly time-consuming process. This design process relies heavily on multi-objective genetic algorithms (MOGA), usually requiring many months of experienced scientists’ time. The main problem lies in having to evaluate numbers of candidate lattices due to the stochastic process of MOGA. Although almost all of these candidates are eventually rejected, they nevertheless require extensive particle tracking to arrive at a Pareto front. We therefore propose a novel Machine Learning (ML) pipeline that nonlinear tracking is replaced by two well-trained neural networks (NNs) to predict dynamic aperture (DA) and momentum aperture (MA) for any lattice candidate. Initial training of these models takes only several minutes on conventional CPUs while predictions are then rendered near instantaneously. We present this novel method and demonstrate the resulting orders of magnitude speedup of the ML-enhanced MOGA process on a 2-DoF problem as well as first results on a more complex 11-DoF problem.
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Funding |
This research is funded by the US Department of Energy(BES & ASCR Programs), and supported by the Director of the Office of Science of the US Department of Energy under Contract No. DEAC02-05CH11231. |
Paper |
download MOPAB106.PDF [0.665 MB / 4 pages] |
Poster |
download MOPAB106_POSTER.PDF [0.918 MB] |
Export |
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Conference |
IPAC2021 |
Series |
International Particle Accelerator Conference (12th) |
Location |
Campinas, SP, Brazil |
Date |
24-28 May 2021 |
Publisher |
JACoW Publishing, Geneva, Switzerland |
Editorial Board |
Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany) |
Online ISBN |
978-3-95450-214-1 |
Online ISSN |
2673-5490 |
Received |
19 May 2021 |
Accepted |
01 June 2021 |
Issue Date |
18 August 2021 |
DOI |
doi:10.18429/JACoW-IPAC2021-MOPAB106 |
Pages |
387-390 |
Copyright |
Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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