Non-standard trajectories found by machine learning for evaporative cooling of 87Rb atoms
| Autor: | Takumi Nakaso, Ryuta Yamamoto, Atsunori Kanemura, Ippei Nakamura, Takeshi Fukuhara |
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| Rok vydání: | 2019 |
| Předmět: |
Condensed Matter::Quantum Gases
Materials science business.industry Bayesian optimization Evaporation 02 engineering and technology Mechanics 021001 nanoscience & nanotechnology 01 natural sciences Atomic and Molecular Physics and Optics law.invention 010309 optics Optics law Laser cooling 0103 physical sciences Atom Trajectory 0210 nano-technology business Bose–Einstein condensate Evaporative cooler |
| Zdroj: | Optics Express. 27:20435 |
| ISSN: | 1094-4087 |
| DOI: | 10.1364/oe.27.020435 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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