Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

Abstract

The Dynamic Aperture is an important concept for the study of non-linear beam dynamics in a circular accelerator. The DA is defined as the extent of the phase-space region in which the particle’s motion remains bounded over a finite number of turns. Such a region is shaped by the imperfections in the magnetic fields, beam-beam effects, electron lens, electron clouds, and other non-linear effects. The study of the DA provides insight into the mechanisms driving the time evolution of beam losses, which is essential for the operation of existing circular accelerators, such as the CERN Large Hadron Collider, as well as for the design of future ones. The standard approach to numerical evaluation of the DA relies on the ability to accurately track initial conditions, distributed in phase space, on a realistic time scale, and this is computationally demanding. To accelerate the angular DA calculation, we propose the use of a Machine Learning technique for the angular DA regression based on simulated HL-LHC data. We demonstrate the implementation of a Deep Neural Network model by measuring the time and assessing the performance of the angular DA regressor, as well as carrying out studies with various hardware architectures including CPU, GPU, and TPU.

Optimizing dynamic aperture studies with active learning

“…to the model [11]. This was achieved by training a Deep Neural Network (DNN) on a substantial data set of simulated initial conditions.…”

“…Similarly to our previous study [11], we used a Multilayer Perceptron (MLP) [20] as the architecture for our surrogate model. This MLP is responsible for examining accelerator parameters, the polar angle, and tracked turns to regress the angular DA.…”