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

Sep 17, 2026

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Quantum vortices in atomic Bose–Einstein condensates (BECs) are topological defects with quantized circulation, typically detected in experiments via time-of-flight imaging that enlarges their density-depleted cores. This work presents a machine-learning approach that detects vortices in experimental BEC images, emphasizing turbulent condensates where vortices are irregularly distributed. A convolutional neural network (CNN) is trained only on synthetic simulated images, avoiding manual vortex-position labeling as ground truth. The CNN generalizes to real experimental data, enabling efficient analysis of large datasets beyond specific experimental conditions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

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Introduction

Vortex imaging and identification challenges

Prior automated detection methods

Motivation for machine learning and synthetic training data

arXiv :2308 .08405v1 [ cond-mat .quant-gas] 16 Aug 2023 Vortex detection in atomic Bose–Einstein condensates using neural networks trained on synthetic images Myeonghyeon Kim, 1 ,2 Junhwan Kwon, 1 Tenzin Rabga,2 and Y. Shin 1 ,2 ,3 ,∗ 1 Department of Physics and Astronomy, Seoul National University, Seoul 08826, Korea 2 Center for Correlated Electron Systems, Institute for Basic Science, Seoul 08826, Korea 3 Institute of Applied Physics, Seoul National University, Seoul 08826, Korea E-mail: [yishin@snu.ac.kr](yishin@snu.ac.kr) 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. Keywords: quantum vortices, Bose–Einstein condensates, convolutional neural network, synthetic data 1. Introduction Quantum vortices are topological defects in superfluid systems, characterized by phase singularities in the superfluid order parameter. These vortices exhibit quantized circulation of particles around them due to the integer phase winding, leading to a depleted particle density at their cores, reminiscent of the eye of a tornado. Quantum vortices play an important role in various superfluid transport phenomena, and extensive research has been conducted on their dynamic properties indifferent superfluid systems such as liquid helium [1], superconductors [2], and atomic Bose–Einstein condensates (BECs) [3] . Atomic BECs, in particular, offer several advantages in the study of quantum vortex dynamics, as they can be effectively described by the Gross–Pitaevskii equation (GPE) within the mean-field approximation, allowing direct comparisons between theoretical predictions and experimental observations [4] . Additionally, resonant imaging techniques allow for direct visualization of quantum vortices in atomic BECs during a time-of-flight (ToF) expansion, where the vortex core size becomes larger than the imaging device’s resolution [5–7] . Due to these advantages, there has been active research on the properties of quantum vortices in atomic BECs [4, 8] as well as investigations of related phenomena such as rotating BECs [3, 9–12], vortex shedding [13–17], 2D superfluidity [18–21], turbulence [22–27], and spontaneous defect formation [28–31] . These studies have significantly advanced our understanding of the superfluid physics associated with these intriguing topological defects. When imaging vortices in a BEC, the vortex cores must be identified through appropriate image analysis. One simple method is manual counting by a human observer; however, this can be exceedingly time-consuming when dealing with large datasets, especially in BEC samples containing numerous vortices. To address this issue, previous studies have developed several image processing algorithms for automated detection of density-depleted cores. In rotating BECs, because vortices form a lattice structure with distinct separation, Gaussian and Laplacian filters can readily locate local density minima, which coincide with single vortex cores [32] . However, in turbulent BE

FAQs

Why are quantum vortices important and how are they characterized in BECs?

Quantum vortices are topological defects in superfluid systems, characterized by phase singularities and quantized circulation due to integer phase winding, producing depleted density at vortex cores. In atomic BECs, these features can be visualized during time-of-flight expansion.

What makes vortex detection difficult in turbulent Bose–Einstein condensates?

In turbulent BECs, vortices are irregularly distributed and may not be well separated, so a single density-depleted region can contain multiple vortices. Accurately locating vortex-core positions remains a key challenge.

How does the proposed CNN method avoid manual labeling and still detect vortices in real images?

The method trains a convolutional neural network solely on synthetic simulated images, eliminating the need for manual vortex-position labels as ground truth. The trained CNN then achieves accurate detection on real experimental BEC images, supporting analysis of large datasets without being tied to specific experimental conditions.

Sep 17, 2026

Uploaded by Anda