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@inproceedings{roussel:ipac2021-tupab289,
author = {R.J. Roussel and A. Hanuka},
title = {{Towards Hysteresis Aware Bayesian Regression and Optimization}},
booktitle = {Proc. IPAC'21},
pages = {2159--2162},
eid = {TUPAB289},
language = {english},
keywords = {ISAC, experiment, target, controls, operation},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-TUPAB289},
url = {https://jacow.org/ipac2021/papers/tupab289.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-TUPAB289},
abstract = {{Algorithms used today for accelerator optimization assume a simple proportional relationship between an intermediate tuning parameter and the resultant field or mechanism which influences the beam. This neglects the effects of hysteresis, where the magnetic or mechanical response depends not only on the current parameter value, but also on the historical parameter values. This prevents the use of one to one surrogate models, such as Gaussian processes, to assist in optimization when hysteresis effects are not negligible, since identical points in input space no longer correspond to a same point in output space. In this work, we demonstrate how Bayesian inference can be used in conjunction with Gaussian processes to jointly model both the hysteresis cycle of magnetic elements and the beam response. Using this technique we demonstrate how to model the hysteresis cycle of a magnet during accelerator operation in situ by only measuring the beam response, without direct magnetic field measurements. This allows us to quickly build accurate statistical models of the beam response that can be used for rapid tuning of accelerators where hysteresis effects are dominant.}},
}