|THAL04||Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL||803|
Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682.
Over the past several years machine learning has increased in popularity for accelerator applications. We have been exploring the use of machine learning as a diagnostic and tuning tool for transfer line from the AGS to RHIC at Brookhaven National Laboratory. In our work, inverse models are used to either provide feed-forward corrections for beam steering or as a diagnostic to illuminate quadrupole magnets that have excitation errors. In this talk we present results on using machine learning for beam steering optimization for a range of different operating energies. We also demonstrate the use of inverse models for optical error diagnostics. Our results are from studies that use combine simulation and measurement data.
|Slides THAL04 [4.845 MB]|
|DOI •||reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL04|
|About •||Received ※ 10 October 2021 Revised ※ 22 October 2021 Accepted ※ 06 February 2022 Issue date ※ 01 March 2022|
|Cite •||reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)|