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

Abstract

In two-color optical nanofiber-based dipole traps for cold alkali atoms, the trap efficiency depends on the wavelength and intensity of light in the evanescent field, and the initial laser-cooling process. Typically, no more than one atom can be trapped per trapping site. Improving the trapping efficiency can increase the number of filled trapping sites, thereby increasing the optical depth. Here, we report on the implementation of an in-loop stochastic artificial neural network machine learner to trap 87 Rb atoms in an uncompensated two-color evanescent field dipole trap by optimizing the absorption of a near-resonant, nanofiber-guided, probe beam. By giving the neural network control of the laser cooling process, we observe an increase in the number of dipole-trapped atoms by ∼ 50%, a small decrease in their average temperature from 150 µK to 140 µK, and an increase in peak optical depth by 70%. The machine learner is able to quickly and effectively explore the large parameter space of the laser cooling control to find optimal parameters for loading the dipole traps. The increased number of atoms should facilitate studies of collective atom-light interactions mediated via the evanescent field.

Rubidium atom spectral lineshapes in high intensity light fields near an optical nanofibre

“…We focus our discussion on ONFs, which provide tight confinement of light beyond the Rayleigh range and also serve as an ideal channel for light propagation. ONFs have been used to efficiently probe, manipulate and trap cold, alkali atoms [6][7][8][9][10] with recent works extending atom and ONF studies to the observation of a quadrupole transition in 87 Rb atoms [11], the demonstration of quadrature squeezing of nanofibreguided light [12], and a determination of the polarization dependency of spin selection in laser-cooled atoms [13].…”