| Title |
Optimizing Beam Dynamics in LHC with Active Deep Learning |
| Authors |
- D. Di Croce, T. Pieloni, M. Seidel
EPFL, Lausanne, Switzerland
- M. Giovannozzi, F.F. Van der Veken
CERN, Meyrin, Switzerland
- E. Krymova
SDSC, Lausanne, Switzerland
- M. Seidel
PSI, Villigen PSI, Switzerland
|
| Abstract |
The Dynamic Aperture (DA) is an important concept for the study of non-linear beam dynamics in a circular accelerator. It refers to the region in phase space where a particle’s motion remains bounded over a given number of turns. Understanding the features of DA is crucial for operating circular accelerators as it provides insights on non-linear beam dynamics and the phenomena affecting beam lifetime. The standard approach to calculate the DA is computationally very intensive. In our study, we aim at determining an optimal set of parameters that affect DA, like betatron tune, chromaticity, and Landau octupole strengths, using a Deep Neural Network (DNN) model. The DNN model predicts the so-called angular DA, based on simulated LHC data. To enhance its performance, we integrated the DNN model into an innovative Active Learning (AL) framework. This framework not only enables retraining and updating of the model, but also facilitates efficient data generation through smart sampling. The results demonstrate that the use of the Active Learning (AL) framework allows faster scanning of LHC ring configuration parameters without compromising the accuracy of the DA calculations.
|
| Paper |
download THAFP09.PDF [0.886 MB / 4 pages] |
| Slides |
download THAFP09_TALK.PDF [1.028 MB] |
| Poster |
download THAFP09_POSTER.PDF [6.173 MB] |
| Cite |
download ※ BibTeX
※ LaTeX
※ Text/Word
※ RIS
※ EndNote |
| Conference |
HB2023 |
| Series |
ICFA Advanced Beam Dynamics Workshop on High-Intensity and High-Brightness Hadron Beams (68th) |
| Location |
Geneva, Switzerland |
| Date |
09-13 October 2023 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Volker R.W. Schaa (GSI, Darmstadt, Germany); Jan Chrin (PSI, Villigen, Switzerland); Massimo Giovannozzi (CERN, Geneva, Switzerland); Dong Eon Kim (PAL, Pohang, South Korea); Marten H. Koopmans (CERN, Geneva, Switzerland); Anton Lechner (CERN, Geneva, Switzerland); Philippe Schoofs (CERN, Geneva, Switzerland) |
| Online ISBN |
978-3-95450-253-0 |
| Online ISSN |
2673-5571 |
| Received |
01 October 2023 |
| Revised |
04 October 2023 |
| Accepted |
10 October 2023 |
| Issued |
31 October 2023 |
| DOI |
doi:10.18429/JACoW-HB2023-THAFP09 |
| Pages |
422-425 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
|
