{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detection-of-oscillation-like-patterns-in","title":"Detection of Oscillation-like Patterns in Eclipsing Binary Light Curves using Neural Network-based Object Detection Algorithms","arxiv_id":"2501.17538","date":"2025-01-29","proceeding":null,"authors":["Burak Ulaş","Tamás Szklenár","Róbert Szabó"],"abstract":"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 involves creating a robust detection framework that can effectively process both synthetic light curves and real observational data. 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 besides 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, followed by testing on unseen {\\it{Kepler}} data to assess their generalization performance. The statistical metrics are also calculated to review the quality of each model. The results indicate that the pre-trained models exhibit high accuracy and reliability in detecting the targeted patterns. Faster R-CNN and You Only Look Once, in particular, showed superior performance in terms of object detection evaluation metrics on the validation dataset such as mAP value exceeding 99\\%. Single Shot MultiBox Detector, on the other hand, is the fastest although it shows slightly lower performance with a mAP of 97\\%. These findings highlight the potential of these models to contribute significantly to the automated determination of pulsating components in eclipsing binary systems, facilitating more efficient and comprehensive astrophysical investigations.","url_abs":"https://arxiv.org/abs/2501.17538v1","url_pdf":"https://arxiv.org/pdf/2501.17538v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"detection-of-oscillation-like-patterns-in","repo_url":"https://github.com/burakulas/code_repo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"detection-of-oscillation-like-patterns-in","repo_url":"https://github.com/burakulas/detocs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}