EBOP MAVEN: A machine learning model for predicting eclipsing binary light curve fitting parameters
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913693275521024 |
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| author | Overall, Stephen Southworth, John |
| author_facet | Overall, Stephen Southworth, John |
| contents | Detached eclipsing binary stars (dEBs) are a key source of data on fundamental stellar parameters. While there is a vast source of candidate systems in the light curve databases of survey missions such as Kepler and TESS, published catalogues of well-characterised systems fall short of reflecting this abundance. We seek to improve the efficiency of efforts to process these data with the development of a machine learning model to inspect dEB light curves and predict the input parameters for subsequent formal analysis by the jktebop code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EBOP MAVEN: A machine learning model for predicting eclipsing binary light curve fitting parameters Overall, Stephen Southworth, John Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Detached eclipsing binary stars (dEBs) are a key source of data on fundamental stellar parameters. While there is a vast source of candidate systems in the light curve databases of survey missions such as Kepler and TESS, published catalogues of well-characterised systems fall short of reflecting this abundance. We seek to improve the efficiency of efforts to process these data with the development of a machine learning model to inspect dEB light curves and predict the input parameters for subsequent formal analysis by the jktebop code. |
| title | EBOP MAVEN: A machine learning model for predicting eclipsing binary light curve fitting parameters |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2502.11758 |