Optimizing loading of cold cesium atoms into a hollow-core fiber using machine learning

TL;DR

This paper demonstrates how machine learning, specifically Gaussian process algorithms via M-LOOP, can optimize the loading of cold cesium atoms into a hollow-core fiber, improving experimental efficiency and control.

Contribution

It introduces the application of ML for optimizing cold atom loading into fibers, showcasing an automated approach that outperforms manual tuning.

Findings

ML achieves better atom-loading conditions than manual scans

Iterative parameter adjustment improves experimental efficiency

ML-assisted optimization is promising for complex cold atom setups

Abstract

Experimental multi-parameter optimization can enhance the interfacing of cold atoms with waveguides and cavities. Recent implementations of machine learning (ML) algorithms demonstrate the optimization of complex cold atom ex perimental sequences in a multi-dimensional parameter space. Here, we report on the use of ML to optimize loading of cold atoms into a hollow-core fiber. We use Gaussian process machine learning in M-LOOP, an open-source online machine learning interface, to perform this optimization. This is implemented by iteratively adjusting experimental parameters based on feedback from an atom-counting measurement of optical "bleaching". We test the effectiveness of ML, alongside a manual scan, to converge to optimal loading conditions. We survey multiple ML runs to auto matically access appreciable atom-loading conditions. In conjunction with experimental design choices, ML-assisted optimization holds promise in the implementation and maintenance of complex cold atom experiments.