JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{edelen:icalepcs2021-thal04,
author = {J.P. Edelen and K.A. Brown and K. Bruhwiler and E.G. Carlin and C.C. Hall and V. Schoefer},
title = {{Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL}},
booktitle = {Proc. ICALEPCS'21},
pages = {803--808},
eid = {THAL04},
language = {english},
keywords = {quadrupole, network, simulation, controls, diagnostics},
venue = {Shanghai, China},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {18},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {03},
year = {2022},
issn = {2226-0358},
isbn = {978-3-95450-221-9},
doi = {10.18429/JACoW-ICALEPCS2021-THAL04},
url = {https://jacow.org/icalepcs2021/papers/thal04.pdf},
abstract = {{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.}},
}