论文标题
恒星参数的深度学习应用确定：II-在AFGK星星的观察光谱中应用
Deep Learning application for stellar parameters determination: II- Application to observed spectra of AFGK stars
论文作者
论文摘要
在这篇后续文件中，我们研究了卷积神经网络从观察到的光谱中得出恒星参数的使用。使用先前确定的超参数，我们构建了一个适用于Teff，Log G，[M/H]和Vesini的神经网络结构。通过将其应用于不同分辨率下的AFGK合成光谱数据库来限制该网络。然后，来自Polarbase，Sophie和Elodie数据库的恒星的参数以及来自太阳能街区恒星的光谱调查的FGK恒星。发现TEFF的恒星参数上的网络模型平均精度为80 K，对数G的0.06 DEX，[m/h]的0.08 dex和vesini的AFGK恒星的3 km/s。
In this follow-up paper, we investigate the use of Convolutional Neural Network for deriving stellar parameters from observed spectra. Using hyperparameters determined previously, we have constructed a Neural Network architecture suitable for the derivation of Teff, log g, [M/H], and vesini. The network was constrained by applying it to databases of AFGK synthetic spectra at different resolutions. Then, parameters of A stars from Polarbase, SOPHIE, and ELODIE databases are derived as well as FGK stars from the Spectroscopic Survey of Stars in the Solar Neighbourhood. The network model average accuracy on the stellar parameters are found to be as low as 80 K for Teff , 0.06 dex for log g, 0.08 dex for [M/H], and 3 km/s for vesini for AFGK stars.
