Shinichi Sunami

Abstract:

APS March Meeting focus session talk

Abstract:

Investigating out-of-equilibrium dynamics with two-dimensional (2D) systems is of widespread theoretical interest, as these systems are strongly influenced by fluctuations and there exists a superfluid phase transition at a finite temperature. In this work, we realise matter-wave interference for degenerate Bose gases, including the first demonstration of coherent splitting of 2D Bose gases using magnetic trapping potentials. We improve the fringe contrast by imaging only a thin slice of the expanded atom clouds, which will be necessary for subsequent studies on the relaxation of the gas following a quantum quench.

Abstract:

Techniques to manipulate the individual constituents of an ultracold mixture are key to investigating impurity physics. In this work, we confine a mixture of hyperfine ground states of 87Rb atoms in a double-well potential. The potential is produced by dressing the atoms with multiple radiofrequencies. The amplitude and phase of each frequency component of the dressing field are controlled to independently manipulate each species. Furthermore, we verify that our mixture of hyperfine states is collisionally stable, with no observable inelastic loss.

Abstract:

We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose–Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (differential evolution), a method based on non-parametric inference (Gaussian process regression) and a gradient-based function approximator (artificial neural network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.