Morphological classification of eclipsing binary stars using computer vision methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F25%3A00639439" target="_blank" >RIV/67985815:_____/25:00639439 - isvavai.cz</a>
Result on the web
<a href="https://hdl.handle.net/11104/0369917" target="_blank" >https://hdl.handle.net/11104/0369917</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ascom.2025.100998" target="_blank" >10.1016/j.ascom.2025.100998</a>
Alternative languages
Result language
angličtina
Original language name
Morphological classification of eclipsing binary stars using computer vision methods
Original language description
We present an application of computer vision methods to classify the light curves of eclipsing binaries (EB). We have used pre-trained models based on convolutional neural networks (ResNet50) and vision transformers (vit_base_patch16_224), which were fine-tuned on images created from synthetic datasets. To improve model generalisation and reduce overfitting, we developed a novel image representation by transforming phase-folded light curves into polar coordinates combined with hexbin visualisation. Our hierarchical approach in the first stage classifies systems into detached and overcontact types, and in the second stage identifies the presence or absence of spots. The binary classification models achieved high accuracy (> 96%) on validation data across multiple passbands (Gaia G, I, and TESS) and demonstrated strong performance (>94%, up to 100% for TESS) when tested on extensive observational data from the OGLE, DEBCat, and WUMaCat catalogues. While the primary binary classification was highly successful, the secondary task of automated spot detection performed poorly, revealing a significant limitation of our models for identifying subtle photometric features. This study highlights the potential of computer vision for EB morphological classification in large-scale surveys, but underscores the need for further research into robust, automated spot detection.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10308 - Astronomy (including astrophysics,space science)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Astronomy and Computing
ISSN
2213-1337
e-ISSN
2213-1345
Volume of the periodical
53
Issue of the periodical within the volume
Oct.
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
9
Pages from-to
100998
UT code for WoS article
001562206000001
EID of the result in the Scopus database
2-s2.0-105014424054