Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/100782
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Type: Journal article
Title: Fast machine-learning online optimization of ultra-cold-atom experiments
Author: Wigley, P.
Everitt, P.
Van Den Hengel, A.
Bastian, J.
Sooriyabandara, M.
Mcdonald, G.
Hardman, K.
Quinlivan, C.
Manju, P.
Kuhn, C.
Petersen, I.
Luiten, A.
Hope, J.
Robins, N.
Hush, M.
Citation: Scientific Reports, 2016; 6(1):25890-1-25890-6
Publisher: Nature
Issue Date: 2016
ISSN: 2045-2322
2045-2322
Statement of
Responsibility: 
P. B. Wigley, P. J. Everitt, A. van den Hengel, J. W. Bastian, M. A. Sooriyabandara, G. D. McDonald, K. S. Hardman, C. D. Quinlivan, P. Manju, C. C. N. Kuhn, I. R. Petersen, A. N. Luiten, J. J. Hope, N. P. Robins, M. R. Hush
Abstract: We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.
Rights: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
RMID: 0030048086
DOI: 10.1038/srep25890
Grant ID: http://purl.org/au-research/grants/arc/DP140101779
http://purl.org/au-research/grants/arc/FT120100291
http://purl.org/au-research/grants/arc/FL110100020
Appears in Collections:Computer Science publications

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