论文标题
使用单波段光学图像检索带有深度学习的星系的内部运动学
Retrieving Internal Kinematics of Galaxies with Deep Learning using Single-Band Optical Images
论文作者
论文摘要
使用深度机器学习，我们表明，可以从使用SDSS-MANGA调查观察到的4596系统训练的光学图像中检索星系的内部速度。我们仅使用$ i $ band图像，我们表明，可以将速度分散和星系的旋转速度进行测量，其精度为29 km〜 $ \ rm {s}^{ - 1} $和69 km〜 $ \ $ \ $ \ rm {s}}^{ - 1}^{ - 1} $，近距离设定限制的数据。这表明光学中的星系结构具有有关星系内部特性的重要信息，并且星系的内部运动学在其恒星光分布中被定量地反映在简单的旋转与分散剂区别之外。
Using deep machine learning we show that the internal velocities of galaxies can be retrieved from optical images trained using 4596 systems observed with the SDSS-MaNGA survey. Using only $i$-band images we show that the velocity dispersions and the rotational velocities of galaxies can be measured to an accuracy of 29 km~$\rm{s}^{-1}$ and 69 km~$\rm{s}^{-1}$ respectively, close to the resolution limit of the spectroscopic data. This shows that galaxy structures in the optical holds important information concerning the internal properties of galaxies and that the internal kinematics of galaxies are quantitatively reflected in their stellar light distributions beyond a simple rotational vs. dispersion distinction.
