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| URL | https://doi.org/10.18429/JACoW-LINAC2024-MOPB081 |
|---|---|
| Title | Generalization ability of convolutional neural networks trained for coherent synchrotron radiation computations |
| Authors |
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| Abstract | Coherent synchrotron radiation has a significant impact on electron storage rings and bunch compressors, inducing energy spread and emittance growth in a bunch. Calculating the effects are computationally expensive, severally limiting the use of simulations. Here, we explore utilizing neural networks (NNs) to model the 3D wakefields of electrons in circular orbit in the steady state condition. NN models were trained on both Gaussian and more general bunch distributions, which evaluate much faster than physics-based simulations. Here, we explore how well the models generalize, by testing their ability to 1) extrapolate to Gaussians with smaller/larger widths 2) predict on distributions never encountered before (out of distribution generalization) using smoothed uniform cubes. We see the models are able to generalize, which makes them potentially useful in the design and optimization of accelerator apparatuses by enabling rapid searches through parameter space. |
| Paper | download: MOPB081.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 32nd Linear Accelerator Conference (LINAC2024) |
| Series | |
| Location | Chicago, IL, USA |
| Date | 25-30 Aug 2024 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Kelly Jaje (ANL), Kip Bishofberger (LANL), Ryan Brody (ANL), Gustavo Bruno (ANL), Jan Chrin (PSI), Cathy Eyberger (ANL), Maggie Montes (SLAC), Denise Skiadopoulos (ANL), Ling Wang (MSU/FRIB), Kent Wootton (ANL) |
| Online ISBN | 978-3-95450-219-6 |
| Online ISSN | 2226-0366 |
| Received | 20 August 2024 |
| Revised | 30 August 2024 |
| Accepted | 30 August 2024 |
| Issued | 23 October 2024 |
| DOI | 10.18429/JACoW-LINAC2024-MOPB081 |
| Pages | 219-222 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |