Eclipsing binary classification with machine learning techniques
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910075680980992 |
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| author | Keskin, Bedri Baştürk, Özgür |
| author_facet | Keskin, Bedri Baştürk, Özgür |
| contents | We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for manual analysis. By using machine learning to classify eclipsing binary stars based on light curve morphology, this study aims to contribute to the efforts to efficiently process and accurately interpret massive data from projects Kepler, TESS and Gaia missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25408 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Eclipsing binary classification with machine learning techniques Keskin, Bedri Baştürk, Özgür Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for manual analysis. By using machine learning to classify eclipsing binary stars based on light curve morphology, this study aims to contribute to the efforts to efficiently process and accurately interpret massive data from projects Kepler, TESS and Gaia missions. |
| title | Eclipsing binary classification with machine learning techniques |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2603.25408 |