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

TL;DR

This paper demonstrates how a neural network-based machine learner optimizes laser cooling parameters to significantly increase the number of trapped cold rubidium atoms in nanofiber-based dipole traps, enhancing optical depth and trapping efficiency.

Contribution

It introduces an in-loop neural network control system for optimizing laser cooling in nanofiber traps, achieving substantial improvements in atom trapping and optical properties.

Findings

50% increase in trapped atoms

70% increase in optical depth

Temperature decrease from 150 μK to 140 μK

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 $\sim$ 50%, a small decrease in their average temperature from 150 $\mu$K to 140 $\mu$K, 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.