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dc.contributor.authorWigley, PB
dc.contributor.authorEveritt, PJ
dc.contributor.authorvan den Hengel, A
dc.contributor.authorBastian, JW
dc.contributor.authorSooriyabandara, MA
dc.contributor.authorMcDonald, GD
dc.contributor.authorHardman, KS
dc.contributor.authorQuinlivan, CD
dc.contributor.authorManju, P
dc.contributor.authorKuhn, CCN
dc.contributor.authorPetersen, IR
dc.contributor.authorLuiten, AN
dc.contributor.authorHope, JJ
dc.contributor.authorRobins, NP
dc.contributor.authorHush, MR
dc.date.accessioned2020-12-22T03:49:29Z
dc.date.available2020-12-22T03:49:29Z
dc.date.issued2016-05-16
dc.identifierpii: srep25890
dc.identifier.citationWigley, 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.
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/11343/257937
dc.description.abstractWe 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.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.titleFast machine-learning online optimization of ultra-cold-atom experiments.
dc.typeJournal Article
dc.identifier.doi10.1038/srep25890
melbourne.affiliation.departmentUniversity General
melbourne.source.titleScientific Reports
melbourne.source.volume6
melbourne.source.issue1
melbourne.source.pages25890-
dc.rights.licenseCC BY
melbourne.elementsid1181544
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867626
melbourne.contributor.authorvan den Hengel, Anton
dc.identifier.eissn2045-2322
melbourne.accessrightsOpen Access


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