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Vortex detection in atomic Bose-Einstein condensates using neural networks trained on synthetic images

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Title
Vortex detection in atomic Bose-Einstein condensates using neural networks trained on synthetic images
Author(s)
Myeonghyeon Kim; Kwon, Junhwan; Tenzin Rabga; Y. Shin
Publication Date
2023-12
Journal
Machine Learning: Science and Technology, v.4, no.4
Publisher
Institute of Physics
Abstract
Quantum vortices in atomic Bose-Einstein condensates (BECs) are topological defects characterized by quantized circulation of particles around them. In experimental studies, vortices are commonly detected by time-of-flight imaging, where their density-depleted cores are enlarged. In this work, we describe a machine learning-based method for detecting vortices in experimental BEC images, particularly focusing on turbulent condensates containing irregularly distributed vortices. Our approach employs a convolutional neural network (CNN) trained solely on synthetic simulated images, eliminating the need for manual labeling of the vortex positions as ground truth. We find that the CNN achieves accurate vortex detection in real experimental images, thereby facilitating analysis of large experimental datasets without being constrained by specific experimental conditions. This novel approach represents a significant advancement in studying quantum vortex dynamics and streamlines the analysis process in the investigation of turbulent BECs. © 2023 The Author(s). Published by IOP Publishing Ltd
URI
https://pr.ibs.re.kr/handle/8788114/14549
DOI
10.1088/2632-2153/ad03ad
ISSN
2632-2153
Appears in Collections:
Center for Correlated Electron Systems(강상관계 물질 연구단) > 1. Journal Papers (저널논문)
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