Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Dark soliton detection using persistent homology

Leykam Daniel, Rondon Irving, Angelakis Dimitrios

Simple record


URIhttp://purl.tuc.gr/dl/dias/DD112E37-A1C2-4866-AA4A-8B688F8AC72B-
Identifierhttps://doi.org/10.1063/5.0097053-
Identifierhttps://pubs.aip.org/aip/cha/article/32/7/073133/2835989/Dark-soliton-detection-using-persistent-homology-
Languageen-
Extent7 pagesen
TitleDark soliton detection using persistent homologyen
CreatorLeykam Danielen
CreatorRondon Irvingen
CreatorAngelakis Dimitriosen
CreatorΑγγελακης Δημητριοςel
PublisherAIP Publishingen
DescriptionThis research was supported in part by the Polisimulator project co-financed by Greece and the EU Regional Development Fund.en
Content SummaryClassifying 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.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-02-23-
Date of Publication2022-
SubjectPersistent homology-based approachen
SubjectObject detection problemsen
Bibliographic CitationD. Leykam, I. Rondón and D. G. Angelakis, “Dark soliton detection using persistent homology,” Chaos, vol. 32, no. 7, July 2022, doi: 10.1063/5.0097053.en

Services

Statistics