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In two-color optical nanofiber-based dipole traps for cold alkali atoms, thetrap efficiency depends on the wavelength and intensity of light in theevanescent field, and the initial laser-cooling process. Typically, no morethan one atom can be trapped per trapping site. Improving the trappingefficiency can increase the number of filled trapping sites, thereby increasingthe optical depth. Here, we report on the implementation of an in-loopstochastic artificial neural network machine learner to trap $^{87}$Rb atoms inan uncompensated two-color evanescent field dipole trap by optimizing theabsorption of a near-resonant, nanofiber-guided, probe beam. By giving theneural network control of the laser cooling process, we observe an increase inthe number of dipole-trapped atoms by $\sim$ 50%, a small decrease in theiraverage temperature from 150 $\mu$K to 140 $\mu$K, and an increase in peakoptical depth by 70%. The machine learner is able to quickly and effectivelyexplore the large parameter space of the laser cooling control to find optimalparameters for loading the dipole traps. The increased number of atoms shouldfacilitate studies of collective atom-light interactions mediated via theevanescent field.

Machine learner optimization of optical nanofiber-based dipole traps for cold $^{87}$Rb atoms