Detecting wide binaries using machine learning algorithms
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917367190126592 |
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| author | Ashesh, Amoy Kaur, Harsimran Aashish, Sandeep |
| author_facet | Ashesh, Amoy Kaur, Harsimran Aashish, Sandeep |
| contents | We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. Our approach incorporates data preprocessing techniques such as SMOTE, correlation analysis, and PCA, and achieves high accuracy and recall in the task of wide binary classification. The resulting publicly available code enables rapid, scalable, and customizable analysis of wide binaries, complementing conventional analyses and providing a valuable resource for future astrophysical studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting wide binaries using machine learning algorithms Ashesh, Amoy Kaur, Harsimran Aashish, Sandeep Astrophysics of Galaxies General Relativity and Quantum Cosmology We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. Our approach incorporates data preprocessing techniques such as SMOTE, correlation analysis, and PCA, and achieves high accuracy and recall in the task of wide binary classification. The resulting publicly available code enables rapid, scalable, and customizable analysis of wide binaries, complementing conventional analyses and providing a valuable resource for future astrophysical studies. |
| title | Detecting wide binaries using machine learning algorithms |
| topic | Astrophysics of Galaxies General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2506.19942 |