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Conference on Lasers and Electro-Optics

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

Gal Ness, Anastasiya Vainbaum, Constantine Shkedrov, Yanay Florshaim, Yoav Sagi

Published Jan 1, 2020• 0 citations
Publisher

Abstract

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.
DOI
10.1364/cleo_qels.2020.fw4c.7

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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).
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.
Single-shot images are the standard readout of experiments with ultracold atoms, the imperfect reflection of their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here we demonstrate how artificial neural networks can optimize this extraction. In contrast to standard averaging approaches, machine learning allows both one- and two-particle densities to be accurately obtained from a drastically reduced number of single-shot images. Quantum fluctuations and correlations are directly harnessed to obtain physical observables for bosons in a tilted double-well potential at an extreme accuracy. Strikingly, machine learning also enables a reliable extraction of momentum-space observables from real-space single-shot images and vice versa. With this technique, the reconfiguration of the experimental setup between in situ and time-of-flight imaging is required only once to obtain training data, thus potentially granting an outstanding reduction in resources.
The ability to detect atoms in high spatiotemporal resolution provides a powerful tool for us to investigate the quantum properties of ultracold quantum gases. Plenty of useful imaging methods, including absorption imaging, phase contrast imaging and fluorescence imaging, have been implemented in detecting atoms. Among them, absorption imaging is the most widely used method in cold atoms laboratory. However, the traditional absorption imaging method is affected by perturbations such as interference between optical elements, fluctuation of laser power, frequency, and spatial position, resulting in residual spatially structured noise and degradation of imaging quality. Especially for regions with lower density or for longer time-of-flight, a large number of repetitions are often required to obtain better signal-to-noise ratio, which would increase the time cost and induce other noise. One must reduce the time between two imaging pulses to suppress the spatial noise. A better charge coupled device (CCD) with higher frame transfer rate or other method like fast-kinetic mode will be used to improve the imaging quality. In this paper, a single-shot cold atom imaging method based on machine learning is proposed, in which only one absorption imaging of cold atoms is required, and the corresponding background image can be generated through the neural network of an autoencoder. This effectively reduces the spatial striped noise in imaging, significantly improves the imaging quality, and makes it possible for cold atoms to be imaged multiple times in a single cycle.
Abstract We use a deep neural network (NN) to detect and place region-of-interest (ROI) boxes around ultracold atom clouds in absorption and fluorescence images—with the ability to identify and bound multiple clouds within a single image. The NN also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds’ Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing. The method developed performs significantly better than a more conventional method based on a standardized image analysis library (Scikit-image) both for identifying ROI and extracting Gaussian parameters.
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.