Non-standard trajectories found by machine learning for evaporative cooling of Rb atoms.

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2019
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Abstract
We present a machine-learning experiment involving evaporative cooling of gaseous Rb atoms. The evaporation trajectory was optimized to maximize the number of atoms cooled down to a Bose-Einstein condensate using Bayesian optimization. After 300 trials within 3 hours, Bayesian optimization discovered trajectories that achieved atom numbers comparable with those of manual tuning by a human expert. Analysis of the machine-learned trajectories revealed minimum requirements for successful evaporative cooling. We found that the manually obtained curve and the machine-learned trajectories were quite similar in terms of evaporation efficiency, although the manual and machine-learned evaporation ramps were significantly different.
Reference Key
nakamura2019nonstandardoptics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nakamura, Ippei;Kanemura, Atsunori;Nakaso, Takumi;Yamamoto, Ryuta;Fukuhara, Takeshi;
Journal Optics express
Year 2019
DOI
10.1364/OE.27.020435
URL
Keywords
bose-einstein condensate condensed matter physics fluid dynamics superradiance vortex

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