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RIS citation export for THAL04: Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL

AU  - Edelen, J.P.
AU  - Brown, K.A.
AU  - Bruhwiler, K.
AU  - Carlin, E.G.
AU  - Hall, C.C.
AU  - Schoefer, V.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - 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.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 803
EP  - 808
KW  - quadrupole
KW  - network
KW  - simulation
KW  - controls
KW  - diagnostics
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-THAL04
UR  - https://jacow.org/icalepcs2021/papers/thal04.pdf
ER  -