Dr Elliot Bentine

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.

Abstract:

Radiofrequency (RF)-dressed potentials are a promising technique for manipulating atomic mixtures, but so far little work has been undertaken to understand the collisions of atoms held within these traps. In this work, we dress a mixture of 85Rb and 87Rb with RF radiation, characterize the inelastic loss that occurs, and demonstrate species-selective manipulations. Our measurements show the loss is caused by two-body 87Rb+85Rb collisions, and we show the inelastic rate coefficient varies with detuning from the RF resonance. We explain our observations using quantum scattering calculations, which give reasonable agreement with the measurements. The calculations consider magnetic fields both perpendicular to the plane of RF polarization and tilted with respect to it. Our findings have important consequences for future experiments that dress mixtures with RF fields.