Detection of Contact Binary Candidates Observed By TESS Using Autoencoder Neural Network
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| Format: | Preprint |
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2024
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| _version_ | 1866909164082561024 |
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| author | Ding, Xu Song, ZhiMing Wang, ChuanJun Ji, KaiFan |
| author_facet | Ding, Xu Song, ZhiMing Wang, ChuanJun Ji, KaiFan |
| contents | Contact binary may be the progenitor of a red nova that eventually produces a merger event and have a cut-off period around 0.2 days. Therefore, a large number of contact binaries is needed to search for the progenitor of red novae and to study the characteristics of short-period contact binaries. In this paper, we employ the Phoebe program to generate a large number of light curves based on the fundamental parameters of contact binaries. Using these light curves as samples, an autoencoder model is trained, which can reconstruct the light curves of contact binaries very well. When the error between the output light curve from the model and the input light curve is large, it may be due to other types of variable stars. The goodness of fit (R2) between the output light curve from the model and the input light curve is calculated. Based on the thresholds for global goodness of fit (R2), period, range magnitude, and local goodness of fit (R2), a total of 1322 target candidates were obtained. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06424 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detection of Contact Binary Candidates Observed By TESS Using Autoencoder Neural Network Ding, Xu Song, ZhiMing Wang, ChuanJun Ji, KaiFan Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Contact binary may be the progenitor of a red nova that eventually produces a merger event and have a cut-off period around 0.2 days. Therefore, a large number of contact binaries is needed to search for the progenitor of red novae and to study the characteristics of short-period contact binaries. In this paper, we employ the Phoebe program to generate a large number of light curves based on the fundamental parameters of contact binaries. Using these light curves as samples, an autoencoder model is trained, which can reconstruct the light curves of contact binaries very well. When the error between the output light curve from the model and the input light curve is large, it may be due to other types of variable stars. The goodness of fit (R2) between the output light curve from the model and the input light curve is calculated. Based on the thresholds for global goodness of fit (R2), period, range magnitude, and local goodness of fit (R2), a total of 1322 target candidates were obtained. |
| title | Detection of Contact Binary Candidates Observed By TESS Using Autoencoder Neural Network |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2404.06424 |