CNN-Based Vortex Detection in Atomic 2D Bose Gases in the Presence of a Phononic Background

TL;DR

This paper introduces a CNN-based method for detecting quantum vortices in 2D Bose gases, overcoming challenges posed by phononic backgrounds, and validates it against experimental data to study vortex behavior near the BKT transition.

Contribution

It develops a machine learning approach trained on simulations to accurately identify vortices in complex experimental images of 2D superfluids, advancing vortex detection techniques.

Findings

CNN accurately detects vortices in noisy backgrounds

Vortex density scaling confirmed across BKT transition

Method bridges simulation and experimental analysis

Abstract

Quantum vortices play a crucial role in both equilibrium and dynamical phenomena in two-dimensional (2D) superfluid systems. Experimental detection of these excitations in 2D ultracold atomic gases typically involves examining density depletions in absorption images, however the presence of a significant phononic background renders the problem challenging, beyond the capability of simple algorithms or the human eye. Here, we utilize a convolutional neural network (CNN) to detect vortices in the presence of strong long- and intermediate-length scale density modulations in finite-temperature 2D Bose gases. We train the model on datasets obtained from ab initio Monte Carlo simulations using the classical-field method for density and phase fluctuations, and Gross-Pitaevskii simulation of realistic expansion dynamics. We use the model to analyze experimental images and benchmark its performance by comparing the results to the matter-wave interferometric detection of vortices, confirming the observed scaling of vortex density across the Berezinskii-Kosterlitz-Thouless (BKT) critical point. The combination of a relevant simulation pipeline with machine-learning methods is a key development towards the comprehensive understanding of complex vortex-phonon dynamics in out-of-equilibrium 2D quantum systems.