Suppression of spontaneous defect formation in inhomogeneous Bose gases

Kim, Myeonghyeon; Rabga, Tenzin; Lee, Yangheon; Goo, Junhong; Bae, Dalmin; Shin, Yeong Gil

doi:10.1103/physreva.106.l061301

“…Our vortex detection method has a number of advantages for the experimental study of vortex dynamics in atomic BECs. CNN's capability to accurately detect vortices in low-resolution experimental images, combined with its high speed, makes it possible to quickly process a large amount of image data, which could be beneficial for statistical investigations and correlation studies on turbulent BECs [56][57][58][59][60]. Furthermore, the synthetic training dataset can be easily adjusted to different experimental and imaging configurations, allowing for broad and rapid applications of the algorithm to various experimental studies.…”

Section: Discussionmentioning

confidence: 99%

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

Kim,

Kwon,

Rabga

et al. 2023

Mach. Learn.: Sci. Technol.

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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.

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“…Our vortex detection method has a number of advantages for the experimental study of vortex dynamics in atomic BECs. CNN's capability to accurately detect vortices in low-resolution experimental images, combined with its high speed, makes it possible to quickly process a large amount of image data, which could be beneficial for statistical investigations and correlation studies on turbulent BECs [56][57][58][59][60]. Furthermore, the synthetic training dataset can be easily adjusted to different experimental and imaging configurations, allowing for broad and rapid applications of the algorithm to various experimental studies.…”

Section: Discussionmentioning

confidence: 99%