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@inproceedings{schenk:ipac2021-tupab216,
author = {M. Schenk and L. Coyle and M. Giovannozzi and E. Krymova and A. Mereghetti and G. Obozinski and T. Pieloni},
% author = {M. Schenk and L. Coyle and M. Giovannozzi and E. Krymova and A. Mereghetti and G. Obozinski and others},
% author = {M. Schenk and others},
title = {{Modeling Particle Stability Plots for Accelerator Optimization Using Adaptive Sampling}},
booktitle = {Proc. IPAC'21},
pages = {1923--1926},
eid = {TUPAB216},
language = {english},
keywords = {network, simulation, collider, resonance, dynamic-aperture},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-TUPAB216},
url = {https://jacow.org/ipac2021/papers/tupab216.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-TUPAB216},
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.}},
}