CERN Accelerating science

Article
Report number	arXiv:2402.11077 ; JINST_168P_1223
Title	Optimizing dynamic aperture studies with active learning
Author(s)	Di Croce, D. (EPFL, Lausanne, FSL) ; Giovannozzi, M. (CERN) ; Krymova, E. (Zurich, ETH) ; Pieloni, T. (EPFL, Lausanne, FSL) ; Redaelli, S. (CERN) ; Seidel, M. (EPFL, Lausanne, FSL ; PSI, Villigen) ; Tomás, R. (CERN) ; Van der Veken, F.F. (CERN)
Publication	2024-04-04
Imprint	2024-02-16
Number of pages	14
In:	JINST 19 (2024) P04004
In:	68th ICFA Advanced Beam Dynamics workshop on high-Intensity and High-Brightness Hadron Beams (HB 2023), CERN, Geneva, Switzerland, 9 - 13 Oct 2023, pp.P04004
DOI	10.1088/1748-0221/19/04/P04004
Subject category	physics.acc-ph ; Accelerators and Storage Rings
Abstract	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.
Copyright/License	preprint: (License: arXiv nonexclusive-distrib 1.0) publication: © 2024 The Author(s) (License: CC-BY-4.0)

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