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RIS citation export for THAL02: Bayesian Techniques for Accelerator Characterization and Control

AU  - Roussel, R.J.
AU  - Edelen, A.L.
AU  - Gonzalez-Aguilera, J.P.
AU  - Kim, Y.K.
AU  - Mayes, C.E.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Bayesian Techniques for Accelerator Characterization and Control
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  - Accelerators and other large experimental facilities are complex, noisy systems that are difficult to characterize and control efficiently. Bayesian statistical modeling techniques are well suited to this task, as they minimize the number of experimental measurements needed to create robust models, by incorporating prior, but not necessarily exact, information about the target system. Furthermore, these models inherently take into account noisy and/or uncertain measurements and can react to time-varying systems. Here we will describe several advanced methods for using these models in accelerator characterization and optimization. First, we describe a method for rapid, turn-key exploration of input parameter spaces using little-to-no prior information about the target system. Second, we highlight the use of Multi-Objective Bayesian optimization towards efficiently characterizing the experimental Pareto front of a system. Throughout, we describe how unknown constraints and parameter modification costs are incorporated into these algorithms.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 791
EP  - 796
KW  - experiment
KW  - target
KW  - controls
KW  - simulation
KW  - solenoid
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-THAL02
UR  - https://jacow.org/icalepcs2021/papers/thal02.pdf
ER  -