Dual-species atomic absorption image reconstruction using deep neural networks

TL;DR

This paper introduces a deep learning-based online image completion method that effectively suppresses interference fringes in optical absorption images of dual-species atomic systems, adaptable to changing experimental conditions.

Contribution

It presents a novel transfer learning approach for real-time fringe suppression in dual-species atomic imaging using deep neural networks.

Findings

Robust fringe suppression across different atomic species.

Efficient online adaptation to experimental drifts.

Easy integration into laboratory workflows.

Abstract

Optical imaging plays an instrumental role in understanding the behavior of trapped neutral atoms. In this work, we describe a deep learning-based online image completion protocol that reduces interference fringes in optical absorption signals for a dual-species atomic system. Regardless of the distinct nature of the task for two different atomic species, 6Li and 23Na, the method displays a robust solution for suppressing fringes. To incorporate this into daily operations, a transfer learning scheme is required that incrementally updates the previously learned parameters. We outline an online image completion method that efficiently adapts to drifting experimental conditions. Our method can be easily integrated into lab settings, where transfer learning can accelerate image analysis.