Modelization of an injector with machine learning

Research output: Contribution to report/book/conference proceedingsIn-proceedings paper

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Modelization of an injector with machine learning. / Gatera, Angélique; Debongnie, Mathieu; Bouly, Frédéric; Baylac, Maud; Chauvin, Nicolas; Uriot, Didier.

10th International Particle Accelerator Conference: IPAC2019, Melbourne, Australia. JACoW Publishing, 2019. p. 3096-3099.

Research output: Contribution to report/book/conference proceedingsIn-proceedings paper

Harvard

Gatera, A, Debongnie, M, Bouly, F, Baylac, M, Chauvin, N & Uriot, D 2019, Modelization of an injector with machine learning. in 10th International Particle Accelerator Conference: IPAC2019, Melbourne, Australia. JACoW Publishing, pp. 3096-3099, 2019 - IPAC, Melbourne, Australia, 2019-05-19. https://doi.org/10.18429/JACoW-IPAC2019-WEPTS006

APA

Gatera, A., Debongnie, M., Bouly, F., Baylac, M., Chauvin, N., & Uriot, D. (2019). Modelization of an injector with machine learning. In 10th International Particle Accelerator Conference: IPAC2019, Melbourne, Australia (pp. 3096-3099). JACoW Publishing. https://doi.org/10.18429/JACoW-IPAC2019-WEPTS006

Vancouver

Gatera A, Debongnie M, Bouly F, Baylac M, Chauvin N, Uriot D. Modelization of an injector with machine learning. In 10th International Particle Accelerator Conference: IPAC2019, Melbourne, Australia. JACoW Publishing. 2019. p. 3096-3099 https://doi.org/10.18429/JACoW-IPAC2019-WEPTS006

Author

Gatera, Angélique ; Debongnie, Mathieu ; Bouly, Frédéric ; Baylac, Maud ; Chauvin, Nicolas ; Uriot, Didier. / Modelization of an injector with machine learning. 10th International Particle Accelerator Conference: IPAC2019, Melbourne, Australia. JACoW Publishing, 2019. pp. 3096-3099

Bibtex - Download

@inproceedings{0ac3cda2c9d446b78a14bd772d83c91e,
title = "Modelization of an injector with machine learning",
abstract = "Modern particle accelerator projects, such as the accelerator for the Multi-purpose hYbrid Research Reactor for High-tech Application (MYRRHA) project driven by the SCK*CEN in Belgium, have very high stability and/or reliability requirements. This means that new strategies for the control systems have to be developed. For that, having faster beam dynamics simulation could prove to be helpful. In this paper, we report the training of neural networks to model key properties of the beam in the MYRRHA injector as well as in IPHI (“Injecteur de Proton {\`a} Haute Intensit{\'e}”). The trained models are shown to be able to reproduce the general behaviours of the machines while requiring a very low computation time.",
keywords = "Machine Learning, neural network, MINERVA, injector transmission",
author = "Ang{\'e}lique Gatera and Mathieu Debongnie and Fr{\'e}d{\'e}ric Bouly and Maud Baylac and Nicolas Chauvin and Didier Uriot",
note = "Score=3",
year = "2019",
month = "5",
day = "23",
doi = "10.18429/JACoW-IPAC2019-WEPTS006",
language = "English",
pages = "3096--3099",
booktitle = "10th International Particle Accelerator Conference",
publisher = "JACoW Publishing",

}

RIS - Download

TY - GEN

T1 - Modelization of an injector with machine learning

AU - Gatera, Angélique

AU - Debongnie, Mathieu

AU - Bouly, Frédéric

AU - Baylac, Maud

AU - Chauvin, Nicolas

AU - Uriot, Didier

N1 - Score=3

PY - 2019/5/23

Y1 - 2019/5/23

N2 - Modern particle accelerator projects, such as the accelerator for the Multi-purpose hYbrid Research Reactor for High-tech Application (MYRRHA) project driven by the SCK*CEN in Belgium, have very high stability and/or reliability requirements. This means that new strategies for the control systems have to be developed. For that, having faster beam dynamics simulation could prove to be helpful. In this paper, we report the training of neural networks to model key properties of the beam in the MYRRHA injector as well as in IPHI (“Injecteur de Proton à Haute Intensité”). The trained models are shown to be able to reproduce the general behaviours of the machines while requiring a very low computation time.

AB - Modern particle accelerator projects, such as the accelerator for the Multi-purpose hYbrid Research Reactor for High-tech Application (MYRRHA) project driven by the SCK*CEN in Belgium, have very high stability and/or reliability requirements. This means that new strategies for the control systems have to be developed. For that, having faster beam dynamics simulation could prove to be helpful. In this paper, we report the training of neural networks to model key properties of the beam in the MYRRHA injector as well as in IPHI (“Injecteur de Proton à Haute Intensité”). The trained models are shown to be able to reproduce the general behaviours of the machines while requiring a very low computation time.

KW - Machine Learning

KW - neural network

KW - MINERVA

KW - injector transmission

UR - https://ecm.sckcen.be/OTCS/llisapi.dll/overview/39469631

U2 - 10.18429/JACoW-IPAC2019-WEPTS006

DO - 10.18429/JACoW-IPAC2019-WEPTS006

M3 - In-proceedings paper

SP - 3096

EP - 3099

BT - 10th International Particle Accelerator Conference

PB - JACoW Publishing

ER -

ID: 6871827