Stochastic optimization of a cold atom experiment using a genetic algorithm

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dc.creatorRohringer, Wolfgang
dc.creatorBuecker, Robert
dc.creatorManz, Stephanie
dc.creatorBetz, Thomas
dc.creatorKoller, Christian
dc.creatorGoebel, Martin
dc.creatorPerrin, Aurelien
dc.creatorSchmiedmayer, Joerg
dc.creatorSchumm, Thorsten
dc.date2008-10-24
dc.date2009-01-15
dc.date.accessioned2026-07-07T12:52:35Z
dc.date.available2026-07-07T12:52:35Z
dc.descriptionWe employ an evolutionary algorithm to automatically optimize different stages of a cold atom experiment without human intervention. This approach closes the loop between computer based experimental control systems and automatic real time analysis and can be applied to a wide range of experimental situations. The genetic algorithm quickly and reliably converges to the most performing parameter set independent of the starting population. Especially in many-dimensional or connected parameter spaces the automatic optimization outperforms a manual search.
dc.description4 pages, 3 figures
dc.identifierhttps://arxiv.org/abs/0810.4474
dc.identifierhttp://arxiv.org/abs/0810.4474
dc.identifierApplied Physics Letters 93, 264101 (2008)
dc.identifierdoi:10.1063/1.3058756
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/223341
dc.subjectAtomic Physics
dc.titleStochastic optimization of a cold atom experiment using a genetic algorithm
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