| Title |
Modelization of an Injector With Machine Learning |
| Authors |
- M. Debongnie, M.A. Baylacpresenter, F. Bouly
LPSC, Grenoble Cedex, France
- N. Chauvin, D. Uriot
CEA-IRFU, Gif-sur-Yvette, France
- A. Gatera
SCK•CEN, Mol, Belgium
- T. Junquera
Accelerators and Cryogenic Systems, Orsay, France
|
| Abstract |
Modern particle accelerator projects, such as MYRRHA, have very high stability and/or reliability requirements. To meet those, it is necessary to optimize or develop new methods for the control systems. One of the difficulties lies in the relatively long computation time of current beam dynamics codes. In this context, the very low computation time of neural network is of great attraction. However, a neural network has to be trained in order to be of any use. The training of a beam dynamic predictor uses a large dataset (experimental or simulated) that represents the dynamics over the parameter space of interest. Therefore, choosing the right training dataset is crucial for the quality of the neural network predictions. In this work, a study on the sampling choice for the training data is performed to train a neural network to predict the transmission of a beam through a low energy beam transport line and a Radiofrequency Quadrupole. We show and discuss the results obtained on training data set to model the IPHI and MYRRHA injectors.
|
| Footnotes & References |
https://myrrha.be/ |
| Paper |
download WEPTS006.PDF [1.049 MB / 4 pages] |
| Export |
download ※ BibTeX
※ LaTeX
※ Text/Word
※ RIS
※ EndNote |
| Conference |
IPAC2019 |
| Series |
International Particle Accelerator Conference (10th) |
| Location |
Melbourne, Australia |
| Date |
19-24 May 2019 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Mark Boland (UoM, Saskatoon, SK, Canada); Hitoshi Tanaka (KEK, Tsukuba, Japan); David Button (ANSTO, Kirrawee, NSW, Australia); Rohan Dowd (ANSTO, Kirrawee, NSW, Australia); Volker RW Schaa (GSI, Darmstadt, Germany); Eugene Tan (ANSTO, Kirrawee, NSW, Australia) |
| Online ISBN |
978-3-95450-208-0 |
| Received |
15 May 2019 |
| Accepted |
23 May 2019 |
| Issue Date |
21 June 2019 |
| DOI |
doi:10.18429/JACoW-IPAC2019-WEPTS006 |
| Pages |
3096-3099 |
| 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. |
|
