Fast, accurate, and predictive method for atom detection in site-resolved images of microtrap arrays

TL;DR

This paper presents a new estimation theory-based method for atom detection in site-resolved images of microtrap arrays, significantly improving accuracy and speed over previous techniques, enabling real-time analysis and scalability.

Contribution

The authors introduce a novel detection method rooted in estimation theory that outperforms Wiener deconvolution in accuracy, scales efficiently, and provides a predictive signal-to-noise ratio measure.

Findings

Detection accuracy is drastically improved over Wiener deconvolution.

Runtime scales linearly and remains below 100 ms for 100x100 arrays.

Signal-to-noise ratio effectively predicts detection error rate.

Abstract

We introduce a new method, rooted in estimation theory, to detect individual atoms in site-resolved images of microtrap arrays, such as optical lattices or optical tweezers arrays. Using labelled test images, we demonstrate drastic improvement of the detection accuracy compared to the popular method based on Wiener deconvolution when the inter-site distance is comparable to the radius of the point spread function. The runtime of our method scales approximately linearly with the number of sites, and remains well below 100 ms for an array of 100 x 100 sites on a desktop computer. It is therefore fully compatible with a real-time usage. Finally, we propose a rigorous definition for the signal-to-noise ratio of the problem, and show that it can be used as a predictor for the detection error rate. Our work opens the prospect for future experiments with increased array sizes, or reduced inter-site distances.