Document Type
Article
Journal/Book Title/Conference
Quantum Physics
Publisher
Cornell University
Publication Date
7-9-2025
Journal Article Version
Version of Record
First Page
1
Last Page
5
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
The quantum state of ultracold atoms is often determined through measurement of the spatial distribution of the atom cloud. Absorption imaging of the cloud is regularly used to extract this spatial information. Accurate determination of the parameters which describe the spatial distribution of the cloud is crucial to the success of many ultracold atom applications. In this work, we present modified deep learning image classification models for image regression. To overcome challenges in data collection, we train the model on simulated absorption images. We compare the performance of the deep learning models to least-squares techniques and show that the deep learning models achieve accuracy similar to least-squares, while consuming significantly less computation time. We compare the performance of models which take a single atom image against models which use an atom image plus other images that contain background information, and find that both models achieved similar accuracy. The use of single image models will enable single-exposure absorption imaging, which simplifies experiment design and eases imaging hardware requirements. The code used to train and evaluate these models is open-source.
Recommended Citation
Morrey, Jacob; Peterson, Isaac; Leonard, Robert H.; Wilson, Joshua M.; Fonta, Francisco; Squires, Matthew B.; and Olson, Spencer E., "Deep Learning for Absorption-Image Analysis" (2025). Space Dynamics Laboratory Publications. Paper 356.
https://digitalcommons.usu.edu/sdl_pubs/356