Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2506.04517

Deep Learning for Absorption-Image Analysis

Jacob Morrey, I. Peterson, Robert H. Leonard, Wilson, Joshua M., Francisco Fonta, Matthew B. Squires, Spencer E. Olson

Published Jun 4, 2025• 0 citations
arXivHTMLPDF

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.

DOI
10.48550/arxiv.2506.04517

Related papers

Single-Exposure Absorption Imaging of Ultracold Atoms Using Deep Learning

Gal Ness, Anastasiya Vainbaum, Constantine Shkedrov +2 more

Absorption imaging is the most common probing technique in experiments with ultracold atoms. The standard procedure involves the division of two frames acquired at successive exposures, one with the atomic absorption signal and one without. A well-known problem is the presence of residual structured noise in the final image, due to small differences between the imaging light in the two exposures. Here, we solve this problem by performing absorption imaging with only a single exposure, where instead of a second exposure the reference frame is generated by an image-completion autoencoder neural network. The network is trained on images without an absorption signal, such that it can infer the noise overlaying the atomic signal based only on the information in the region encircling the signal. We demonstrate our approach on data captured with a quantum degenerate Fermi gas. The average residual noise in the resulting images is below that of the standard double-shot technique. Our method simplifies the experimental sequence, reduces the hardware requirements, and can improve the accuracy of extracted physical observables. The trained network and its generating scripts are available as an open-source repository (absDL.github.io).

arXiv:2003.01643•Physical Review Applied•Jul 6, 2020•29 citations

Deep-learning-enhanced imaging processing in a spinor Bose-Einstein condensate

Jun Jian, Xin Wang, Yong Kun Qin +9 more

Abstract Accurate and swift identification of atomic cloud regions in absorption images is crucial for extracting physical information in ultracold atomic experiments, where absorption imaging plays a critical detection role. This study has devised a novel deep learning-based approach for the identification and extraction of atomic clouds in absorption images of ultracold atoms. The methodology consists of two main components. Initially, a YOLOv5 model is developed to identify atomic cloud regions. This process involves creating a training dataset by extensively collecting data from the sodium Bose-Einstein condensate (BEC) platform and annotating atomic cloud regions in the images. These annotated images are then utilized to train a neural network, resulting in a proficient YOLOv5 model for the initial selection of atomic cloud regions. The second part involves the secondary positioning of the initially identified atomic cloud regions. This step includes conducting a grid search in proximity to the initially identified regions, using goodness-of-fit as a criterion to pinpoint the most suitable atomic cloud regions. The method showcases remarkable efficiency and accuracy in calibrating atomic cloud regions through impressive automated processing, robustness, and generalization capabilities. Its successful application in processing Rabi oscillation images further verifies its reliability based on physical laws.

Physica Scripta•Jun 27, 2025•0 citations

Noise suppression and image enhancement in cold atom absorption imaging

Pengcheng Zheng, Songqian Zhang, Zhu Ma +7 more

The noise in absorption imaging of cold atoms significantly impacts measurement accuracy across a range of applications with ultracold atoms. It is crucial to adopt an approach that offers effective denoising capabilities without compromising the unique structure of atom clouds. Here, we introduce, to our knowledge, a novel image enhancement algorithm for cold atomic absorption imaging. The algorithm successfully suppresses background noise, enhancing image contrast significantly. Experimental results showcase that this approach can improve the image's signal-to-noise ratio by about 10 dB and enhance the uncertainty of cold atom number measurements by approximately tenfold, preserving the spatial distribution of the atom clouds. Moreover, the method exhibits exceptional performance and robustness when confronted with fringe noise and multi-component imaging scenarios, offering high stability. Importantly, this method can perform denoising based on a single image (without the need to establish a reference library), and the optimization process is entirely automated, eliminating the need for manual parameter selection. The method is both compatible and practical, making it applicable to various cold atom absorption imaging fields.

Applied Optics•Dec 2, 2024•2 citations

Single-Shot Absorption Imaging of Ultracold Atoms Using Deep-Neural-Network

Gal Ness, Anastasiya Vainbaum, Constantine Shkedrov +2 more

We demonstrate the use of a deep-neural-network to perform absorption imaging of ultracold atoms in a single exposure. The noise in the resulting images is smaller, hence physical observables can be extracted with better accuracy.

Conference on Lasers and Electro-Optics•Jan 1, 2020•0 citations

Thermometry of simulated Bose–Einstein condensates using machine learning

J. Gwyn Griffiths, Steven A. Wrathmall, S. A. Gardiner

Abstract Precise determination of thermodynamic parameters in ultracold Bose gases remains challenging due to the destructive nature of conventional measurement techniques and inherent experimental uncertainties. We demonstrate a machine learning approach for rapid, minimally destructive estimation of the chemical potential and temperature from a single image of an in situ imaged density profiles of finite-temperature Bose gases. Our convolutional neural network is trained exclusively on quasi-two-dimensional ‘pancake’ condensates in harmonic trap configurations. It achieves parameter extraction within fractions of a second. The model also demonstrates some zero-shot generalisation across both trap geometry and thermalisation dynamics, successfully estimating the temperature (although not the chemical potential) for toroidally trapped condensates with errors of only a few nanokelvin despite no prior exposure to such geometries during training, and maintaining predictive accuracy during dynamic thermalisation processes after a relatively brief evolution without explicit training on non-equilibrium states. These results suggest that supervised learning can overcome traditional limitations in ultracold atom thermometry, with extension to broader geometric configurations, temperature ranges, and additional parameters potentially enabling comprehensive real-time analysis of quantum gas experiments. Such capabilities could significantly streamline experimental workflows whilst improving measurement precision across a range of quantum fluid systems.

arXiv:2506.16925•Machine Learning Science and Technology•Mar 9, 2026•0 citations

Spatial calibration of high-density absorption imaging

Toke Vibel, M B Christensen, M A Kristensen +4 more

Abstract The accurate determination of atom numbers is an ubiquitous problem in the field of ultracold atoms. For modest atom numbers, absolute calibration techniques are available, however, for large numbers and high densities, the available techniques neglect many-body scattering processes. Here, a spatial calibration technique for time-of-flight absorption images of ultracold atomic clouds is presented. The calibration is obtained from radially averaged absorption images and we provide a practical guide to the calibration process. It is shown that the calibration coefficient scales linearly with optical density and depends on the absorbed photon number for the experimental conditions explored here. This allows for the direct inclusion of a spatially dependent calibration in the image analysis. For typical ultracold atom clouds the spatial calibration technique leads to corrections in the detected atom number up to ≈ 12 % and temperature up to ≈ 14 % in comparison to previous calibration techniques. The technique presented here addresses a major difficulty in absorption imaging of ultracold atomic clouds and prompts further theoretical work to understand the scattering processes in ultracold dense clouds of atoms for accurate atom number calibration.

arXiv:2402.06514•Journal of Physics B Atomic Molecular and Optical Physics•Jun 3, 2024•7 citations
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.
Absorption imaging is the most common probing technique in experiments with ultracold atoms. The standard procedure involves the division of two frames acquired at successive exposures, one with the atomic absorption signal and one without. A well-known problem is the presence of residual structured noise in the final image, due to small differences between the imaging light in the two exposures. Here, we solve this problem by performing absorption imaging with only a single exposure, where instead of a second exposure the reference frame is generated by an image-completion autoencoder neural network. The network is trained on images without an absorption signal, such that it can infer the noise overlaying the atomic signal based only on the information in the region encircling the signal. We demonstrate our approach on data captured with a quantum degenerate Fermi gas. The average residual noise in the resulting images is below that of the standard double-shot technique. Our method simplifies the experimental sequence, reduces the hardware requirements, and can improve the accuracy of extracted physical observables. The trained network and its generating scripts are available as an open-source repository (absDL.github.io).
Abstract Accurate and swift identification of atomic cloud regions in absorption images is crucial for extracting physical information in ultracold atomic experiments, where absorption imaging plays a critical detection role. This study has devised a novel deep learning-based approach for the identification and extraction of atomic clouds in absorption images of ultracold atoms. The methodology consists of two main components. Initially, a YOLOv5 model is developed to identify atomic cloud regions. This process involves creating a training dataset by extensively collecting data from the sodium Bose-Einstein condensate (BEC) platform and annotating atomic cloud regions in the images. These annotated images are then utilized to train a neural network, resulting in a proficient YOLOv5 model for the initial selection of atomic cloud regions. The second part involves the secondary positioning of the initially identified atomic cloud regions. This step includes conducting a grid search in proximity to the initially identified regions, using goodness-of-fit as a criterion to pinpoint the most suitable atomic cloud regions. The method showcases remarkable efficiency and accuracy in calibrating atomic cloud regions through impressive automated processing, robustness, and generalization capabilities. Its successful application in processing Rabi oscillation images further verifies its reliability based on physical laws.
The noise in absorption imaging of cold atoms significantly impacts measurement accuracy across a range of applications with ultracold atoms. It is crucial to adopt an approach that offers effective denoising capabilities without compromising the unique structure of atom clouds. Here, we introduce, to our knowledge, a novel image enhancement algorithm for cold atomic absorption imaging. The algorithm successfully suppresses background noise, enhancing image contrast significantly. Experimental results showcase that this approach can improve the image's signal-to-noise ratio by about 10 dB and enhance the uncertainty of cold atom number measurements by approximately tenfold, preserving the spatial distribution of the atom clouds. Moreover, the method exhibits exceptional performance and robustness when confronted with fringe noise and multi-component imaging scenarios, offering high stability. Importantly, this method can perform denoising based on a single image (without the need to establish a reference library), and the optimization process is entirely automated, eliminating the need for manual parameter selection. The method is both compatible and practical, making it applicable to various cold atom absorption imaging fields.
Abstract Precise determination of thermodynamic parameters in ultracold Bose gases remains challenging due to the destructive nature of conventional measurement techniques and inherent experimental uncertainties. We demonstrate a machine learning approach for rapid, minimally destructive estimation of the chemical potential and temperature from a single image of an in situ imaged density profiles of finite-temperature Bose gases. Our convolutional neural network is trained exclusively on quasi-two-dimensional ‘pancake’ condensates in harmonic trap configurations. It achieves parameter extraction within fractions of a second. The model also demonstrates some zero-shot generalisation across both trap geometry and thermalisation dynamics, successfully estimating the temperature (although not the chemical potential) for toroidally trapped condensates with errors of only a few nanokelvin despite no prior exposure to such geometries during training, and maintaining predictive accuracy during dynamic thermalisation processes after a relatively brief evolution without explicit training on non-equilibrium states. These results suggest that supervised learning can overcome traditional limitations in ultracold atom thermometry, with extension to broader geometric configurations, temperature ranges, and additional parameters potentially enabling comprehensive real-time analysis of quantum gas experiments. Such capabilities could significantly streamline experimental workflows whilst improving measurement precision across a range of quantum fluid systems.
Abstract The accurate determination of atom numbers is an ubiquitous problem in the field of ultracold atoms. For modest atom numbers, absolute calibration techniques are available, however, for large numbers and high densities, the available techniques neglect many-body scattering processes. Here, a spatial calibration technique for time-of-flight absorption images of ultracold atomic clouds is presented. The calibration is obtained from radially averaged absorption images and we provide a practical guide to the calibration process. It is shown that the calibration coefficient scales linearly with optical density and depends on the absorbed photon number for the experimental conditions explored here. This allows for the direct inclusion of a spatially dependent calibration in the image analysis. For typical ultracold atom clouds the spatial calibration technique leads to corrections in the detected atom number up to ≈ 12 % and temperature up to ≈ 14 % in comparison to previous calibration techniques. The technique presented here addresses a major difficulty in absorption imaging of ultracold atomic clouds and prompts further theoretical work to understand the scattering processes in ultracold dense clouds of atoms for accurate atom number calibration.