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TY - CONF AU - Hanuka, A. AU - Duris, J.P. AU - Shang, H. AU - Sun, Y. ED - Liu, Lin ED - Byrd, John M. ED - Neuenschwander, Regis T. ED - Picoreti, Renan ED - Schaa, Volker R. W. TI - Demonstration of Machine Learning Front-End Optimization of the Advanced Photon Source Linac 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 - The electron beam for the Advanced Photon Source (APS) at Argonne National Laboratory is generated from a thermionic RF gun and accelerated by an S-band linear accelerator – the APS linac. While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front-end beam dynamics have been difficult to model. One of the issues is that beam properties from thermionic guns can vary. As a result, linac front-end beam tuning is required to establish good matching and maximize the charge transported through the linac. A traditional Nelder-Mead simplex optimizer has been used to find the best settings for the sixteen quadrupoles and steering magnets. However, it takes a long time and requires some fair initial conditions. The Gaussian Process (GP) optimizer does not have the initial condition limitation and runs several times faster. In this paper, we report our data collection and analysis for the training of the GP hyperparameters and discuss the application of GP optimizer on the APS linac front-end optimization for maximum bunch charge transportation efficiency through the linac. PB - JACoW Publishing CP - Geneva, Switzerland SP - 2163 EP - 2166 KW - linac KW - controls KW - gun KW - electron KW - photon DA - 2021/08 PY - 2021 SN - 2673-5490 SN - 978-3-95450-214-1 DO - doi:10.18429/JACoW-IPAC2021-TUPAB290 UR - https://jacow.org/ipac2021/papers/tupab290.pdf ER -