论文标题
估计DA白色矮人星星的大气参数与深度学习
Estimating Atmospheric Parameters of DA White Dwarf Stars with Deep Learning
论文作者
论文摘要
白矮星（WDS）的大气参数的确定对于研究它们至关重要。传统方法是将模型光谱拟合到观察到的吸收线并报告最低$χ^2 $错误的参数，该参数强烈依赖于并不总是公开访问的理论模型。在这项工作中，我们构建了一个深度学习网络，以独立于模型估计DA恒星（DAS）的TEFF和LOG G，该网络对应于具有氢气的大气层的WD。该网络在Sloan Digital Sky Survey（SDSS）中直接训练并测试了完全光波长的归一化磁通像素。测试零件中的实验产生的TEFF和LOG G的根平方误差（RMSE）分别为900 K和0.1 DEX。该技术适用于从5000 K到40000 K的TEFF的DA，并从7.0 DEX到9.0 DEX。此外，使用降级分辨率$ \ sim 200 $验证了此方法的适用性。因此，对于将由中国空间站望远镜（CSST）检测到的DA的分析也是实用的。
The determination of atmospheric parameters of white dwarf stars (WDs) is crucial for researches on them. Traditional methodology is to fit the model spectra to observed absorption lines and report the parameters with the lowest $χ^2$ error, which strongly relies on theoretical models that are not always publicly accessible. In this work, we construct a deep learning network to model-independently estimate Teff and log g of DA stars (DAs), corresponding to WDs with hydrogen dominated atmospheres. The network is directly trained and tested on the normalized flux pixels of full optical wavelength range of DAs spectroscopically confirmed in the Sloan Digital Sky Survey (SDSS). Experiments in test part yield that the root mean square error (RMSE) for Teff and log g approaches to 900 K and 0.1 dex, respectively. This technique is applicable for those DAs with Teff from 5000 K to 40000 K and log g from 7.0 dex to 9.0 dex. Furthermore, the applicability of this method is verified for the spectra with degraded resolution $\sim 200$. So it is also practical for the analysis of DAs that will be detected by the Chinese Space Station Telescope (CSST).
