分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: We developed a convolutional neural network (CNN) model to distinguish the double-lined spectroscopic binaries (SB2s) from others based on single exposure medium-resolution spectra ($R\sim 7,500$). The training set consists of a large set of mock spectra of single stars and binaries synthesized based on the MIST stellar evolutionary model and ATLAS9 atmospheric model. Our model reaches a novel theoretic false positive rate by adding a proper penalty on the negative sample (e.g., 0.12\% and 0.16\% for the blue/red arm when the penalty parameter $\Lambda=16$). Tests show that the performance is as expected and favors FGK-type Main-sequence binaries with high mass ratio ($q \geq 0.7$) and large radial velocity separation ($\Delta v \geq 50\,\mathrm{km\,s^{-1}}$). Although the real false positive rate can not be estimated reliably, validating on eclipsing binaries identified from Kepler light curves indicates that our model predicts low binary probabilities at eclipsing phases (0, 0.5, and 1.0) as expected. The color-magnitude diagram also helps illustrate its feasibility and capability of identifying FGK MS binaries from spectra. We conclude that this model is reasonably reliable and can provide an automatic approach to identify SB2s with period $\lesssim 10$ days. This work yields a catalog of binary probabilities for over 5 million spectra of 1 million sources from the LAMOST medium-resolution survey (MRS), and a catalog of 2198 SB2 candidates whose physical properties will be analyzed in our following-up paper. Data products are made publicly available at the journal as well as our Github website.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: Based on luminosity contributions, we develop a spectroscopic modelling method to derive atmospheric parameters of component stars in binary systems. The method is designed for those spectra of binaries which show double-lined features due to the radial velocities differences between the component stars. We first derive the orbital parameters and the stellar radii by solving the light and radial velocity curves. Then the luminosity contributions in different phases can be calculated. The synthesised double-lined spectra model is constructed by superposing theoretical single-star spectra according to the luminosity contributions. Finally, we derive the atmospheric parameters of each component star by the model fitting method. For multi-epoch double-lined spectra observed by the Large sky Area Multi-Object Spectroscopic Telescope (LAMOST) Medium Resolution Survey ($R \sim 7500$), our method gives robust results for detached eclipsing binary systems observed in different orbital phases. Furthermore, this method can also be applied to other spectroscopic data with different resolutions as long as the systems are detached eclipsing binaries with nearly spherical stars.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: Based on luminosity contributions, we develop a spectroscopic modelling method to derive atmospheric parameters of component stars in binary systems. The method is designed for those spectra of binaries which show double-lined features due to the radial velocities differences between the component stars. We first derive the orbital parameters and the stellar radii by solving the light and radial velocity curves. Then the luminosity contributions in different phases can be calculated. The synthesised double-lined spectra model is constructed by superposing theoretical single-star spectra according to the luminosity contributions. Finally, we derive the atmospheric parameters of each component star by the model fitting method. For multi-epoch double-lined spectra observed by the Large sky Area Multi-Object Spectroscopic Telescope (LAMOST) Medium Resolution Survey ($R \sim 7500$), our method gives robust results for detached eclipsing binary systems observed in different orbital phases. Furthermore, this method can also be applied to other spectroscopic data with different resolutions as long as the systems are detached eclipsing binaries with nearly spherical stars.