Abstract
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.