Deep-learning-based quantum vortex detection in atomic Bose–Einstein condensates. Metz, F., Polo, J., Weber, N., & Busch, T. Machine Learning: Science and Technology, 2(3):035019, June, 2021. Publisher: IOP Publishing![Deep-learning-based quantum vortex detection in atomic Bose–Einstein condensates [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{metz_deep-learning-based_2021,
title = {Deep-learning-based quantum vortex detection in atomic {Bose}–{Einstein} condensates},
volume = {2},
issn = {2632-2153},
url = {https://doi.org/10.1088/2632-2153/abea6a},
doi = {10.1088/2632-2153/abea6a},
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.},
language = {en},
number = {3},
urldate = {2021-11-01},
journal = {Machine Learning: Science and Technology},
author = {Metz, Friederike and Polo, Juan and Weber, Natalya and Busch, Thomas},
month = jun,
year = {2021},
note = {Publisher: IOP Publishing},
pages = {035019},
}
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