一种快速推导晚型相接双星参数的方法及其在Catalina Sky Survey中的应用
A Method of Rapidly Deriving Late-type Contact Binary Parameters and Its Application in the Catalina Sky Survey
AI总结：
本文提出结合神经网络与HMC的NNHMC方法，利用PHOEBE合成光变曲线学习参数映射并推断后验，在Catalina Sky Survey中高效获得19,104个晚型相接双星参数。
摘要：
随着大型光学巡天项目的持续发展，已发布了大量晚型相接双星系统（CBs）的光变曲线。使用WD程序和PHOEBE程序推导CBs参数面临挑战。因此，本研究开发了一种基于神经网络（NN）模型并结合Hamiltonian Monte Carlo（HMC）算法的快速光变曲线参数推导方法（NNHMC）。研究采用神经网络建立参数与PHOEBE程序预先生成的光变曲线之间的映射关系，并利用HMC算法获取参数的后验分布。NNHMC方法被应用于来自Catalina Sky Survey的大型相接双星样本，共推导出19,104个晚型相接双星参数。其中，5172个目标的倾角大于70度且温差小于400 K。针对30个CBs，将所得结果与既往研究进行比较，二者之间的拟合优度（R2）分布基本一致。NNHMC方法具备同时推导大量目标参数的能力。此外，它还能为未来涉及大样本CBs的巡天项目提供一种极其高效的参数快速推导工具。
英文摘要：
With the continuous development of large optical surveys, a large number of light curves of late-type contact binary systems (CBs) have been released. Deriving parameters for CBs using the the WD program and the PHOEBE program poses a challenge. Therefore, this study developed a method for rapidly deriving light curves based on the Neural Networks (NN) model combined with the Hamiltonian Monte Carlo (HMC) algorithm (NNHMC). The neural network was employed to establish the mapping relationship between the parameters and the pregenerated light curves by the PHOEBE program, and the HMC algorithm was used to obtain the posterior distribution of the parameters. The NNHMC method was applied to a large contact binary sample from the Catalina Sky Survey, and a total of 19,104 late-type contact binary parameters were derived. Among them, 5172 have an inclination greater than 70 deg and a temperature difference less than 400 K. The obtained results were compared with the previous studies for 30 CBs, and there was an essentially consistent goodness-of-fit (R2) distribution between them. The NNHMC method possesses the capability to simultaneously derive parameters for a vast number of targets. Furthermore, it can provide an extremely efficient tool for rapid derivation of parameters in future sky surveys involving large samples of CBs.
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