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- W2960886985 abstract "We present a machine-learning experiment involving evaporative cooling of gaseous 87Rb 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." @default.
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- W2960886985 date "2019-07-09" @default.
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- W2960886985 title "Non-standard trajectories found by machine learning for evaporative cooling of <sup>87</sup>Rb atoms" @default.
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- W2960886985 doi "https://doi.org/10.1364/oe.27.020435" @default.
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