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TY - CONF AU - Schirmer, D. ED - Liu, Lin ED - Byrd, John M. ED - Neuenschwander, Regis T. ED - Picoreti, Renan ED - Schaa, Volker R. W. TI - Machine Learning Applied to Automated Tunes Control at the 1.5 GeV Synchrotron Light Source DELTA J2 - Proc. of IPAC2021, Campinas, SP, Brazil, 24-28 May 2021 CY - Campinas, SP, Brazil T2 - International Particle Accelerator Conference T3 - 12 LA - english AB - Machine learning (ML) driven algorithms are finding more and more use cases in the domain of accelerator physics. Apart from correlation analysis in large data volumes, low and high level controls, like beam orbit correction, also non-linear feedback systems are possible application fields. This also includes monitoring the storage ring betatron tunes, as an important task for stable machine operation. For this purpose classical, shallow (non-deep), feed-forward neural networks (NNs) were investigated for automated adjusting the storage ring tunes. The NNs were trained with experimental machine data as well as with simulated data based on a lattice model of the DELTA storage ring. With both data sources comparable tune correction accuracies were achieved, both, in real machine operation and for the simulated storage ring model. In contrast to conventional PID methods, the trained NNs were able to approach the desired target tunes in fewer steps. The report summarizes the current status of this machine learning project and points out possible future improvements as well as other possible applications. PB - JACoW Publishing CP - Geneva, Switzerland SP - 3379 EP - 3382 KW - storage-ring KW - quadrupole KW - simulation KW - controls KW - operation DA - 2021/08 PY - 2021 SN - 2673-5490 SN - 978-3-95450-214-1 DO - doi:10.18429/JACoW-IPAC2021-WEPAB303 UR - https://jacow.org/ipac2021/papers/wepab303.pdf ER -