This preprint has made available a large collection of labelled images of BECs containing 0, 1 or multiple dark solitons. The authors of that study trained a convolutional neural network to distinguish these three classes of images. Single dark solitons could be detected with an accuracy of about 90\%. On 2014 MacBook Pro each image took 2.3s to classify using the neural network. Our aim here is to develop a persistent-homology based approach with superior performance (both in accuracy and run-time). Fast and accurate machine learning-based identification of solitons would enable high-throughput experimental study of their dynamics by eliminating the time-consuming and at times error-prone manual identification of the solitons in the experimentally-obtained images.
Edit (3/8/21): I have a more recent post discussing the finished manuscript resulting from this project.
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