Contact binary stars are important research objects in astrophysics. The calculation speed of deriving the parameters of contact binaries with the Wilson–Devinney program and the Phoebe with Markov chain Monte Carlo (MCMC) program is relatively slow. It is unrealistic to derive the parameters in batches with the program for sky survey data. We obtain a neural network model of supervised learning with the training of synthetic light curves with Phoebe. We calculate the parameters of eight special targets from the simulated data and the Kepler data. Then, we generate the new light curve to fit the light curve of the special target base on these parameters. The correlation index R2 of the fitting result is more than 0.98. The method can be used to fit the target which has orbital inclinations greater than 50°. By fitting the Kepler data and the observed data on the ground, the method has a good generalization ability to these targets, which have some noise and some starspots. The calculation speed of one light curve with this method is less than seconds. We can derive the parameters quickly in batches to undertake some statistical work for sky survey data with the method.

中文翻译：

---

一种导出接触二进制文件参数的机器学习方法

接触双星是天体物理学的重要研究对象。使用 Wilson-Devinney 程序和 Phoebe 使用马尔可夫链 Monte Carlo (MCMC) 程序推导接触二进制参数的计算速度相对较慢。用程序对巡天数据批量推导参数是不现实的。我们通过 Phoebe 对合成光变曲线的训练获得了监督学习的神经网络模型。我们根据模拟数据和开普勒数据计算了八个特殊目标的参数。然后，我们根据这些参数生成新的光变曲线来拟合特殊目标的光变曲线。拟合结果的相关指数R2大于0.98。该方法可用于拟合轨道倾角大于50°的目标。通过对开普勒数据和地面观测数据进行拟合，该方法对这些具有一定噪声和星斑的目标具有良好的泛化能力。用这种方法计算一条光曲线的速度小于秒。利用该方法，我们可以批量快速导出参数，对巡天数据进行一些统计工作。