Deep-learning-based quantum vortex detection in atomic Bose–Einstein condensates. Issue 3 (14th June 2021)

• Record Type: Journal Article
• Authors: Metz, Friederike; Polo, Juan; Weber, Natalya; Busch, Thomas
• Is Part Of: Machine learning: science and technology. Volume 2:Issue 3(2021)
• Publication Date: 2021-06-14
• Subjects: machine learning -- object detection -- convolutional neural network -- vortices -- Bose–Einstein condensate -- non-equilibrium dynamics -- Gross–Pitaevskii equation; 006.31
• DOI: 10.1088/2632-2153/abea6a
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: Quantum vortices naturally emerge in rotating Bose–Einstein condensates (BECs) and, similarly to their classical counterparts, allow the study of a range of interesting out-of-equilibrium phenomena, such as turbulence and chaos. However, the study of such phenomena requires the determination of the precise location of each vortex within a BEC, which becomes challenging when either only the density of the condensate is available or sources of noise are present, as is typically the case in experimental settings. Here, we introduce a machine-learning-based vortex detector motivated by state-of-the-art object detection methods that can accurately locate vortices in simulated BEC density images. Our model allows for robust and real-time detection in noisy and non-equilibrium configurations. Furthermore, the network can distinguish between vortices and anti-vortices if the phase profile of the condensate is also available. We anticipate that our vortex detector will be advantageous for both experimental and theoretical studies of the static and dynamic properties of vortex configurations in BECs.