| Paper |
Title |
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| TUPOPT059 |
Machine Learning Methods for Chromaticity Control at the 1.5 GeV Synchrotron Light Source DELTA |
1141 |
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- D. Schirmer, A. Althaus, T. Schüngel
DELTA, Dortmund, Germany
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In the past, the chromaticity values at the DELTA electron storage ring were manually adjusted using 15 individual sextupole power supply circuits, which are combined into 7 magnet families. To automate and optimize the time-consuming setting process, various machine learning (ML) approaches were investigated. For this purpose, simulations were first performed using a storage ring model and the performance of different neural network (NN) based models was compared. Subsequently, the neural networks were trained with experimental data and successfully implemented for chromaticity correction in real accelerator operation.
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| DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOPT059
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| About • |
Received ※ 20 May 2022 — Revised ※ 11 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 21 June 2022 |
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