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    Fast machine-learning online optimization of ultra-cold-atom experiments.

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    Author
    Wigley, PB; Everitt, PJ; van den Hengel, A; Bastian, JW; Sooriyabandara, MA; McDonald, GD; Hardman, KS; Quinlivan, CD; Manju, P; Kuhn, CCN; ...
    Date
    2016-05-16
    Source Title
    Scientific Reports
    Publisher
    Springer Science and Business Media LLC
    University of Melbourne Author/s
    van den Hengel, Anton
    Affiliation
    University General
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Wigley, P. B., Everitt, P. J., van den Hengel, A., Bastian, J. W., Sooriyabandara, M. A., McDonald, G. D., Hardman, K. S., Quinlivan, C. D., Manju, P., Kuhn, C. C. N., Petersen, I. R., Luiten, A. N., Hope, J. J., Robins, N. P. & Hush, M. R. (2016). Fast machine-learning online optimization of ultra-cold-atom experiments.. Sci Rep, 6 (1), pp.25890-. https://doi.org/10.1038/srep25890.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/257937
    DOI
    10.1038/srep25890
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867626
    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.

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