CERN Accelerating science

Article
Title	Modeling Particle Stability Plots for Accelerator Optimization Using Adaptive Sampling
Related title	MODELING PARTICLE STABILITY PLOTS FOR ACCELERATOR OPTIMIZATION USING ADAPTIVE SAMPLING
Author(s)	Schenk, Michael (Ecole Polytechnique, Lausanne ; CERN) ; Coyle, Loic (CERN ; Ecole Polytechnique, Lausanne) ; Giovannozzi, Massimo (CERN) ; Krymova, Ekaterina (Ecole Polytechnique, Lausanne ; Zurich, ETH) ; Mereghetti, Alessio (CERN ; CNAO, Milan) ; Obozinski, Guillaume (Ecole Polytechnique, Lausanne ; Zurich, ETH) ; Pieloni, Tatiana (CERN ; Ecole Polytechnique, Lausanne)
Publication	Geneva : JACoW, 2021
Number of pages	4
In:	JACoW IPAC 2021 (2021) 1923-1926
In:	12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.1923-1926
DOI	10.18429/JACoW-IPAC2021-TUPAB216 (publication)
Subject category	Accelerators and Storage Rings
Abstract	One key aspect of accelerator optimization is to maximize the dynamic aperture (DA) of a ring. Given the number of adjustable parameters and the compute-intensity of DA simulations, this task can benefit significantly from efficient search algorithms of the available parameter space. We propose to gradually train and improve a surrogate model of the DA from SixTrack simulations while exploring the parameter space with adaptive sampling methods. Here we report on a first model of the particle stability plots using convolutional generative adversarial networks (GAN) trained on a subset of SixTrack numerical simulations for different ring configurations of the Large Hadron Collider at CERN.
Copyright/License	publication: © 2021 (License: CC-BY-3.0)

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