Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators
For any storage ring-based large-scale scientific facility, one of the most important performance parameters is the dynamic aperture (DA), which measures the motion stability of charged particles in a global manner. To date, long-term tracking-based simulation is regarded as the most reliable method...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2022-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ac77ac |
| _version_ | 1797748430334853120 |
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| author | Jinyu Wan Yi Jiao |
| author_facet | Jinyu Wan Yi Jiao |
| author_sort | Jinyu Wan |
| collection | DOAJ |
| description | For any storage ring-based large-scale scientific facility, one of the most important performance parameters is the dynamic aperture (DA), which measures the motion stability of charged particles in a global manner. To date, long-term tracking-based simulation is regarded as the most reliable method to calculate DA. However, numerical tracking may become a significant issue, especially when a plethora of candidate designs of a storage ring need to be evaluated. In this paper, we present a novel machine learning-based method, which can reduce the computation cost of DA tracking by approximately one order of magnitude, while keeping sufficiently high evaluation accuracy. Moreover, we demonstrate that this method is independent of concrete physical models of a storage ring. This method has the potential to be applied to similar problems of identifying irregular motions in other complex dynamical systems. |
| first_indexed | 2024-03-12T16:04:38Z |
| format | Article |
| id | doaj.art-7b1c1d9b2ae44de5ac1269f3f46fb0b6 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:04:38Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-7b1c1d9b2ae44de5ac1269f3f46fb0b62023-08-09T14:25:15ZengIOP PublishingNew Journal of Physics1367-26302022-01-0124606303010.1088/1367-2630/ac77acMachine learning enabled fast evaluation of dynamic aperture for storage ring acceleratorsJinyu Wan0https://orcid.org/0000-0002-6525-8745Yi Jiao1Key Laboratory of Particle Acceleration Physics and Technology, Institute of High Energy Physics, Chinese Academy of Sciences , Beijing 100049, People’s Republic of China; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaKey Laboratory of Particle Acceleration Physics and Technology, Institute of High Energy Physics, Chinese Academy of Sciences , Beijing 100049, People’s Republic of China; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaFor any storage ring-based large-scale scientific facility, one of the most important performance parameters is the dynamic aperture (DA), which measures the motion stability of charged particles in a global manner. To date, long-term tracking-based simulation is regarded as the most reliable method to calculate DA. However, numerical tracking may become a significant issue, especially when a plethora of candidate designs of a storage ring need to be evaluated. In this paper, we present a novel machine learning-based method, which can reduce the computation cost of DA tracking by approximately one order of magnitude, while keeping sufficiently high evaluation accuracy. Moreover, we demonstrate that this method is independent of concrete physical models of a storage ring. This method has the potential to be applied to similar problems of identifying irregular motions in other complex dynamical systems.https://doi.org/10.1088/1367-2630/ac77acstorage ringdynamic aperturemotion stabilitymachine learningcomplex dynamical system |
| spellingShingle | Jinyu Wan Yi Jiao Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators New Journal of Physics storage ring dynamic aperture motion stability machine learning complex dynamical system |
| title | Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators |
| title_full | Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators |
| title_fullStr | Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators |
| title_full_unstemmed | Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators |
| title_short | Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators |
| title_sort | machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators |
| topic | storage ring dynamic aperture motion stability machine learning complex dynamical system |
| url | https://doi.org/10.1088/1367-2630/ac77ac |
| work_keys_str_mv | AT jinyuwan machinelearningenabledfastevaluationofdynamicapertureforstorageringaccelerators AT yijiao machinelearningenabledfastevaluationofdynamicapertureforstorageringaccelerators |