Machine-learning-accelerated Bose-Einstein condensation
Machine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of ^{87}Rb atoms by Bayesian optimization of up to 55 control parameters. This approach enabl...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2022-12-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.4.043216 |
| _version_ | 1797210590027972608 |
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| author | Zachary Vendeiro Joshua Ramette Alyssa Rudelis Michelle Chong Josiah Sinclair Luke Stewart Alban Urvoy Vladan Vuletić |
| author_facet | Zachary Vendeiro Joshua Ramette Alyssa Rudelis Michelle Chong Josiah Sinclair Luke Stewart Alban Urvoy Vladan Vuletić |
| author_sort | Zachary Vendeiro |
| collection | DOAJ |
| description | Machine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of ^{87}Rb atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of 2.8×10^{3} optically trapped ^{87}Rb atoms from a room-temperature gas in 575 ms. The algorithm achieves the fast BEC preparation by applying highly efficient Raman cooling to near quantum degeneracy, followed by a brief final evaporation. We anticipate that many other physics experiments with complex nonlinear system dynamics can be significantly enhanced by a similar machine-learning approach. |
| first_indexed | 2024-04-24T10:13:00Z |
| format | Article |
| id | doaj.art-858d34a86e1c46dc8c17645571c9dcd2 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:13:00Z |
| publishDate | 2022-12-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-858d34a86e1c46dc8c17645571c9dcd22024-04-12T17:27:19ZengAmerican Physical SocietyPhysical Review Research2643-15642022-12-014404321610.1103/PhysRevResearch.4.043216Machine-learning-accelerated Bose-Einstein condensationZachary VendeiroJoshua RametteAlyssa RudelisMichelle ChongJosiah SinclairLuke StewartAlban UrvoyVladan VuletićMachine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of ^{87}Rb atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of 2.8×10^{3} optically trapped ^{87}Rb atoms from a room-temperature gas in 575 ms. The algorithm achieves the fast BEC preparation by applying highly efficient Raman cooling to near quantum degeneracy, followed by a brief final evaporation. We anticipate that many other physics experiments with complex nonlinear system dynamics can be significantly enhanced by a similar machine-learning approach.http://doi.org/10.1103/PhysRevResearch.4.043216 |
| spellingShingle | Zachary Vendeiro Joshua Ramette Alyssa Rudelis Michelle Chong Josiah Sinclair Luke Stewart Alban Urvoy Vladan Vuletić Machine-learning-accelerated Bose-Einstein condensation Physical Review Research |
| title | Machine-learning-accelerated Bose-Einstein condensation |
| title_full | Machine-learning-accelerated Bose-Einstein condensation |
| title_fullStr | Machine-learning-accelerated Bose-Einstein condensation |
| title_full_unstemmed | Machine-learning-accelerated Bose-Einstein condensation |
| title_short | Machine-learning-accelerated Bose-Einstein condensation |
| title_sort | machine learning accelerated bose einstein condensation |
| url | http://doi.org/10.1103/PhysRevResearch.4.043216 |
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