Inspiration from machine learning on example of optimization of the Bose-Einstein condensate of thulium atoms in a 1064-nm trap
arXiv:2311.06795 · doi:10.1103/PhysRevA.109.033313
Abstract
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 machine learning technique. Nevertheless, this approach usually does not give any insight in the underlying physics. Here we demonstrate possibility to learn physics from the 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 limitation imposed by a 3-body recombination process. This limitation was successfully overcome by leveraging Fano-Feshbach resonances.
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