Optimizing dynamic aperture studies with active learning Article Swipe
D. Di Croce
,
M. Giovannozzi
,
Ekaterina Krymova
,
Tatiana Pieloni
,
Stefano Redaelli
,
M. Seidel
,
Rogelio Tomás
,
Frederik F. Van der Veken
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1748-0221/19/04/p04004
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1748-0221/19/04/p04004
Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the features of dynamic aperture is crucial for the design and operation of such accelerators, as it provides insights into nonlinear effects and the possibility of optimising beam lifetime. The standard approach to calculate the dynamic aperture requires numerical simulations of several initial conditions densely distributed in phase space for a sufficient number of turns to probe the time scale corresponding to machine operations. This process is very computationally intensive and practically outside the range of today's computers. In our study, we introduced a novel method to estimate dynamic aperture rapidly and accurately by utilising a Deep Neural Network model. This model was trained with simulated tracking data from the CERN Large Hadron Collider and takes into account variations in accelerator parameters such as betatron tune, chromaticity, and the strength of the Landau octupoles. To enhance its performance, we integrate the model into an innovative Active Learning framework. This framework not only enables retraining and updating of the computed model, but also facilitates efficient data generation through smart sampling. Since chaotic motion cannot be predicted, traditional tracking simulations are incorporated into the Active Learning framework to deal with the chaotic nature of some initial conditions. The results demonstrate that the use of the Active Learning framework allows faster scanning of the configuration parameters without compromising the accuracy of the dynamic aperture estimates.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1748-0221/19/04/p04004
- https://iopscience.iop.org/article/10.1088/1748-0221/19/04/P04004/pdf
- OA Status
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- Cited By
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- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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- DOI
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https://doi.org/10.1088/1748-0221/19/04/p04004Digital Object Identifier
- Title
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Optimizing dynamic aperture studies with active learningWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-01Full publication date if available
- Authors
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D. Di Croce, M. Giovannozzi, Ekaterina Krymova, Tatiana Pieloni, Stefano Redaelli, M. Seidel, Rogelio Tomás, Frederik F. Van der VekenList of authors in order
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https://doi.org/10.1088/1748-0221/19/04/p04004Publisher landing page
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https://iopscience.iop.org/article/10.1088/1748-0221/19/04/P04004/pdfDirect link to full text PDF
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hybridOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1748-0221/19/04/P04004/pdfDirect OA link when available
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Computer science, Aperture (computer memory), Physics, AcousticsTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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