Machine-learning classification of two-dimensional vortex configurations

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Abstract
We consider computer-generated configurations of quantized vortices in planar superfluid Bose-Einstein condensates. We show that unsupervised machine-learning technology can successfully be used for classifying such vortex configurations to identify prominent vortex phases of matter. The machine-learning approach could thus be applied for automatically classifying large data sets of vortex configurations obtainable by experiments on two-dimensional quantum turbulence.
Funding Information
  • Australian Research Council

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