LANL, Los Alamos, New Mexico, USA
SLAC, Menlo Park, California, USA
CSU, Fort Collins, Colorado, USA
[1] A. A. Lutman et al., Nat. Photonics 10.11, 745 (2016).
[2] V. Yakimenko et al., IPAC2016, Busan, Korea, 2016.
| Paper | Title | Page |
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| THYGBE1 | Applying Artificial Intelligence to Accelerators | 2925 |
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Particle accelerators are being designed and operated over a wide range of complex beam phase space distributions. For example, the Linac Coherent Light Source (LCLS) upgrade, LCLS-II, is considering complex schemes such as two-color operation [1], while the plasma wake field acceleration facility for advanced accelerator experimental tests (FACET) upgrade, FACET-II, is planning on providing custom tailored current profiles [2]. Because of uncertainty due to limited diagnostics and time varying performance, such as thermal drifts, as well as collective effects and the complex coupling of large numbers of components, it is impossible to use simple look up tables for parameter settings in order to quickly switch between widely varying operating ranges. Several forms of artificial intelligence are currently being investigated in order to enable accelerators to quickly and automatically re-adjust component settings without human intervention. In this work we discuss recent progress in applying neural networks and adaptive feedback algorithms to enable automatic accelerator tuning and optimization.
[1] A. A. Lutman et al., Nat. Photonics 10.11, 745 (2016). [2] V. Yakimenko et al., IPAC2016, Busan, Korea, 2016. |
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Slides THYGBE1 [14.256 MB] | |
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE1 | |
| Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |