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

TL;DR

This paper presents a CNN-based method trained on synthetic images for accurate vortex detection in experimental Bose-Einstein condensate images, enabling large-scale analysis without manual labeling.

Contribution

It introduces a neural network trained solely on synthetic data to detect vortices in real BEC images, removing the need for manual annotation.

Findings

CNN accurately detects vortices in experimental images

Method works well on turbulent condensates with irregular vortex distributions

Enables large-scale, automated analysis of BEC vortex data

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.