Detection of oscillation-like patterns in eclipsing binary light curves using neural network-based object detection algorithms

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dc.contributor.authorUlas, B.
dc.contributor.authorSzklenar, T.
dc.contributor.authorSzabo, R.
dc.date.accessioned2025-05-29T02:57:48Z
dc.date.available2025-05-29T02:57:48Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractAims. The primary aim of this research is to evaluate several convolutional neural network-based object detection algorithms for identifying oscillation-like patterns in light curves of eclipsing binaries. This involved creating a robust detection framework that can effectively process both synthetic light curves and real observational data. Methods. The study employs several state-of-the-art object detection algorithms, including Single Shot MultiBox Detector, Faster Region-based Convolutional Neural Network, You Only Look Once, and EfficientDet, as well as a custom non-pretrained model implemented from scratch. Synthetic light curve images and images derived from observational TESS light curves of known eclipsing binaries with a pulsating component were constructed with corresponding annotation files using custom scripts. The models were trained and validated on established datasets, which was followed by testing on unseen Kepler data to assess their generalisation performance. The statistical metrics were also calculated to review the quality of each model. Results. The results indicate that the pre-trained models exhibit high accuracy and reliability in detecting the targeted patterns. The Faster Region-based Convolutional Neural Network and You Only Look Once in particular showed superior performance in terms of object detection evaluation metrics on the validation dataset, including a mean average precision value exceeding 99%. The Single Shot MultiBox Detector, on the other hand, is the fastest, although it shows a slightly lower performance, with a mean average precision of 97%. These findings highlight the potential of these models to significantly contribute to the automated determination of pulsating components in eclipsing binary systems and thus facilitate more efficient and comprehensive astrophysical investigations.
dc.description.sponsorshipTrkiye Bilimsel ve Teknolojik Arascedil;timath;rma Kurumuhttp://dx.doi.org/10.13039/501100004410 [2219, 1059B192202496, SNN-147362]; TUBITAK (The Scientific and Technological Research Council of Turkey); Hungarian Research, Development and Innovation Office (NKFIH); NASA Explorer Program [NAS 5-26555, 2022]; NASA; NASA Science Mission Directorate
dc.description.sponsorshipBU acknowledges the financial support of TUBITAK (The Scientific and Technological Research Council of Turkey) within the framework of the 2219 International Postdoctoral Research Fellowship Program (No. 1059B192202496). Hospitality of the HUN-REN CSFK Konkoly Observatory is greatly appreciated. RSz and TSz acknowledge the support of the SNN-147362 grant of the Hungarian Research, Development and Innovation Office (NKFIH). The numerical calculations reported in this paper were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources). This paper includes data collected with the TESS mission, obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the TESS mission is provided by the NASA Explorer Program. STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. This paper includes data collected by the Kepler mission and obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the Kepler mission is provided by the NASA Science Mission Directorate. STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. This work made use of Astropy (http://www.astropy.org): a community-developed core Python package and an ecosystem of tools and resources for astronomy (Astropy Collaboration 2013, 2018, 2022). This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. The authors acknowledge OpenAI's ChatGPT for its assistance in debugging certain codes used in this research, with all suggested corrections carefully reviewed and validated by the authors before implementation.
dc.identifier.doi10.1051/0004-6361/202452020
dc.identifier.issn0004-6361
dc.identifier.issn1432-0746
dc.identifier.scopus2-s2.0-86000652514
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1051/0004-6361/202452020
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30182
dc.identifier.volume695
dc.identifier.wosWOS:001440585900002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEdp Sciences S A
dc.relation.ispartofAstronomy & Astrophysics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250529
dc.subjectmethods: data analysis
dc.subjecttechniques: image processing
dc.subjectbinaries: eclipsing
dc.subjectstars: oscillations
dc.titleDetection of oscillation-like patterns in eclipsing binary light curves using neural network-based object detection algorithms
dc.typeArticle

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