https://doi.org/10.1140/epjp/s13360-021-01348-5
Regular Article
Supervised learning-based reconstruction of magnet errors in circular accelerators
1
CERN, 1211, Geneva 23, Switzerland
2
Johann-Wolfgang Goethe University, 60438, Frankfurt am Main, Germany
3
GSI Helmholtzzentrum für Schwerionenforschung, 64291, Darmstadt, Germany
Received:
23
December
2020
Accepted:
23
March
2021
Published online:
7
April
2021
Magnetic field errors and misalignments cause optics perturbations, which can lead to machine safety issues and performance degradation. The correlation between magnetic errors and deviations of the measured optics functions from design can be used in order to build supervised learning models able to predict magnetic errors directly from a selection of measured optics observables. Extending the knowledge of errors in individual magnets offers potential improvements of beam control by including this information into optics models and corrections computation. Besides, we also present a technique for denoising and reconstruction of measurements data, based on autoencoder neural networks and linear regression. We investigate the usefulness of supervised machine learning algorithms for beam optics studies in a circular accelerator such as the LHC, for which the presented method has been applied in simulated environment, as well as on experimental data.
© The Author(s) 2021
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