2025/07/15 by Paul Anderson, Anderson, Paul, Sreesh Venuturumilli +3
Engineering · Materials Science · Physics and Astronomy · #Atomic Physics (physics.atom-ph) #FOS: Physical sciences #Nuclear Materials and Properties #Nuclear Physics and Applications #Nuclear reactor physics and engineering
paper · pdf · doi:10.48550/arxiv.2507.11519
openalex publication_date 2025/07/15 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28
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.