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arXiv:2003.00346 · Physical Review A

Machine learning for achieving Bose-Einstein condensation of thulium atoms

E. T. Davletov, V. V. Tsyganok, Vladimir Khlebnikov, D. A. Pershin, D. V. Shaykin, Alexey V. Akimov

Published Jul 14, 2020• 57 citations
arXivHTMLPDF

Abstract

Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which is related to quantum simulations. Various problems may benefit from different atomic species, but cooling down novel species interesting for quantum simulations to BEC temperatures requires a substantial amount of optimization and is usually considered to be a difficult experimental task. In this work, we implemented the Bayesian machine learning technique to optimize the evaporative cooling of thulium atoms and achieved BEC in an optical dipole trap operating near 532 nm. The developed approach could be used to cool down other novel atomic species to quantum degeneracy without additional studies of their properties.

DOI
10.1103/physreva.102.011302

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Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which is related to quantum simulations. Various problems may benefit from different atomic species, but cooling down novel species interesting for quantum simulations to BEC temperatures requires a substantial amount of optimization and is usually considered to be a difficult experimental task. In this work, we implemented the Bayesian machine learning technique to optimize the evaporative cooling of thulium atoms and achieved BEC in an optical dipole trap operating near 532 nm. The developed approach could be used to cool down other novel atomic species to quantum degeneracy without additional studies of their properties.
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 87Rb^{87}\mathrm{Rb}87Rb atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of $2.8\ifmmode\times\else\texttimes\fi{}{10}^{3}$ optically trapped 87Rb^{87}\mathrm{Rb}87Rb 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.
The number of atoms in Bose-Einstein condensate determines the scale of experiments that can be performed, making it crucial for quantum simulations. Optimization of the number of atoms in the condensate is a complex problem which could be efficiently solved using the machine learning technique. Nevertheless, this approach usually does not give any insight in the underlying physics. Here we demonstrate the possibility to learn physics from machine learning on an example of condensation of thulium atoms at a 1064-nm dipole trap. Optimization of the number of condensed atoms revealed a saturation, which was explained as a limitation imposed by a three-body recombination process. This limitation was successfully overcome by leveraging Fano-Feshbach resonances.
To achieve Bose-Einstein condensation, one may implement evaporative cooling by dynamically regulating the power of laser beams forming the optical dipole trap. We propose and experimentally demonstrate a protocol of Bayesian optimization of Bose-Einstein condensation via the evaporative cooling model. Applying this protocol, pure Bose-Einstein condensate of 87Rb with 2.4X10e4 atoms can be produced via evaporative cooling from the initial stage when the number of atoms is 6.0X10e5 at a temperature of 12μK. In comparison with Bayesian optimization via blackbox experiment, our protocol only needs a few experiments required to verify some close-to-optimal curves for optical dipole trap laser powers, therefore it greatly saves experimental resources.
Recently, thulium has been condensed to Bose-Einstein condensate. Machine learning was used to avoid a detailed study of all obstacles, making cooling difficult. This paper analyzes the atomic loss mechanism for the 532-nm optical trap, used in the Bose-condensation experiment, and compares it with the alternative and more traditional micrometer-range optical dipole trap. We also measured the scalar and tensor polarizability of thulium at 1064 nm, which were found to be $167\ifmmode\pm\else\textpm\fi{}25$ a.u. ($275\ifmmode\pm\else\textpm\fi{}41\phantom{\rule{0.55542pt}{0ex}}\phantom{\rule{0.55542pt}{0ex}}\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}41}\mathrm{F}\phantom{\rule{0.16em}{0ex}}{\mathrm{m}}^{2}$) and $\ensuremath{-}4\ifmmode\pm\else\textpm\fi{}1$ a.u. ($7\ifmmode\pm\else\textpm\fi{}2\phantom{\rule{0.55542pt}{0ex}}\phantom{\rule{0.55542pt}{0ex}}\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}41}\phantom{\rule{0.28em}{0ex}}\mathrm{F}\phantom{\rule{0.16em}{0ex}}{\mathrm{m}}^{2}$), respectively.
A Bose–Einstein condensate with a large atom number is an important experimental platform for quantum simulation and quantum information research. An optical dipole trap is the a conventional way to hold the ultracold atoms, where an atomic cloud is evaporatively cooled down before reaching the Bose–Einstein condensate. A carefully designed trap depth controlling curve is typically required to realize the optimal evaporation cooling. We present and demonstrate a simple way to optimize the evaporation cooling in a crossed optical dipole trap. A polyline shape optical power control profile is easily obtained with our method, by which a pure Bose–Einstein condensate with atom number 1.73 × 105 is produced. Theoretically, we numerically simulate the optimal evaporation cooling using the parameters of our apparatus based on a kinetic theory. Compared to the simulation results, our evaporation cooling shows a good performance. We believe that our simple method can be used to quickly realize evaporation cooling in optical dipole traps.
Attempts to create quantum degenerate gases without evaporative cooling have been pursued since the early days of laser cooling, with the consensus that polarization gradient cooling (PGC, also known as "optical molasses") alone cannot reach condensation. In the present work, we report that simple PGC can generate a small Bose-Einstein condensate (BEC) inside a corrugated micrometer-sized optical dipole trap. The experimental parameters enabling BEC creation were found by machine learning, which increased the atom number by a factor of 5 and decreased the temperature by a factor of 2.5, corresponding to almost 2 orders of magnitude gain in phase space density. When the trapping light is slightly misaligned through a microscopic objective lens, a BEC of ∼250 ^{87}Rb atoms is formed inside a local dimple within 40 ms of PGC after MOT loading.