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| 001 | 478846 | ||
| 005 | 20230213123254.0 | ||
| 020 | _ | _ | |a 978-3-95450-227-1 |
| 024 | 7 | _ | |a 10.18429/JACoW-IPAC2022-WEPOMS036 |2 doi |
| 024 | 7 | _ | |a Stein:2022atw |2 INSPIRETeX |
| 024 | 7 | _ | |a inspire:2137525 |2 inspire |
| 024 | 7 | _ | |a 10.3204/PUBDB-2022-02796 |2 datacite_doi |
| 037 | _ | _ | |a PUBDB-2022-02796 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Stein, Oliver |0 P:(DE-H253)PIP1014315 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 13th International Particle Accelerator Conference |g IPAC'22 |c Bangkok |d 2022-06-12 - 2022-06-17 |w Thailand |
| 245 | _ | _ | |a Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications |
| 260 | _ | _ | |a [Geneva] |c 2022 |b JACoW Publishing, Geneva, Switzerland |
| 295 | 1 | 0 | |a [Ebook] 13th International Particle Accelerator Conference : June 12-17, 2022, Impact Forum, Muangthong Thani, Bangkok, Thailand : conference proceedings / Chanwattana, Thakonwat , [Geneva] : JACoW Publishing, July 2022, |
| 300 | _ | _ | |a 2330-2333 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 500 | _ | _ | |a Literaturangaben; |
| 520 | _ | _ | |a Machine learning has proven to be a powerful tool with many applications in the field of accelerator physics. Training machine learning models is a highly iterative process that requires large numbers of samples. However, beam time is often limited and many of the available simulation frameworks are not optimized for fast computation. As a result, training complex models can be infeasible. In this contribution, we introduce Cheetah, a linear beam dynamics framework optimized for fast computations. We show that Cheetah outperforms existing simulation codes in terms of speed and furthermore demonstrate the application of Cheetah to a reinforcement-learning problem as well as the successful transfer of the Cheetah-trained model to the real world. We anticipate that Cheetah will allow for faster development of more capable machine learning solutions in the field, one day enabling the development of autonomous accelerators. |
| 536 | _ | _ | |a 621 - Accelerator Research and Development (POF4-621) |0 G:(DE-HGF)POF4-621 |c POF4-621 |f POF IV |x 0 |
| 536 | _ | _ | |a ZT-I-PF-5-6 - Autonomous Accelerator (AA) (2020_ZT-I-PF-5-6) |0 G:(DE-HGF)2020_ZT-I-PF-5-6 |c 2020_ZT-I-PF-5-6 |x 1 |
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| 650 | _ | 7 | |a Accelerator Physics |2 Other |
| 650 | _ | 7 | |a MC5: Beam Dynamics and EM Fields |2 Other |
| 650 | _ | 7 | |a simulation |2 autogen |
| 650 | _ | 7 | |a space-charge |2 autogen |
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| 700 | 1 | _ | |a Agapov, Ilya |0 P:(DE-H253)PIP1011647 |b 1 |
| 700 | 1 | _ | |a Eichler, Annika |0 P:(DE-H253)PIP1087213 |b 2 |
| 700 | 1 | _ | |a Kaiser, Jan |0 P:(DE-H253)PIP1095111 |b 3 |
| 773 | _ | _ | |a 10.18429/JACoW-IPAC2022-WEPOMS036 |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/478846/files/HTML-Approval_of_scientific_publication.html |
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| 856 | 4 | _ | |y OpenAccess |u https://bib-pubdb1.desy.de/record/478846/files/IPAC22_Cheetah.pdf |
| 856 | 4 | _ | |y OpenAccess |u https://bib-pubdb1.desy.de/record/478846/files/wepoms036.pdf |
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| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Materie |l Matter and Technologies |1 G:(DE-HGF)POF4-620 |0 G:(DE-HGF)POF4-621 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-600 |4 G:(DE-HGF)POF |v Accelerator Research and Development |x 0 |
| 914 | 1 | _ | |y 2022 |
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