Dark soliton detection using persistent homologyDark soliton detection using persistent homology Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2024-02-232022enClassifying images often requires manual identification of qualitative features. Machine learning approaches including convolutional neural networks can achieve accuracy comparable to human classifiers but require extensive data and computational resources to train. We show how a topological data analysis technique, persistent homology, can be used to rapidly and reliably identify qualitative features in experimental image data. The identified features can be used as inputs to simple supervised machine learning models, such as logistic regression models, which are easier to train. As an example, we consider the identification of dark solitons using a dataset of 6257 labeled atomic Bose–Einstein condensate density images.This research was supported in part by the Polisimulator project co-financed by Greece and the EU Regional Development Fund.http://creativecommons.org/licenses/by/4.0/Chaos: An Interdisciplinary Journal of Nonlinear Science327 Leykam Daniel Rondon Irving Angelakis Dimitrios Αγγελακης Δημητριος AIP Publishing Persistent homology-based approach Object detection problems