We develop a deep neural network (DNN) to obtain photometry of saturated
stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The DNN can
obtain unbiased photometry for stars from g=4 to 14 mag with a dispersion
(15%-85% 1sigma range around median) of 0.12 mag for saturated (g<11.5 mag)
stars. More importantly, the light curve of a non-variable saturated star has a
median dispersion of only 0.037 mag. The DNN light curves are, in many cases,
spectacularly better than provided by the standard ASAS-SN pipelines. While the
network was trained on g band data from only one of ASAS-SN's 20 cameras,
initial experiments suggest that it can be used for any camera and the older
ASAS-SN V band data as well. The dominant problems seem to be associated with
correctable issues in the ASAS-SN data reduction pipeline for saturated stars
more than the DNN itself. The method is publicly available as a light curve
option on ASAS-SN Sky Patrol v1.0.

我们开发了一个深度神经网络 (DNN)，用于获取全天自动超新星巡天 (ASAS-SN) 中饱和恒星的光度。该 DNN 可以在 g=4 到 14 mag 之间获得无偏光度，饱和 (g<11.5 mag) 恒星的离散度 (15%-85% 中位数附近的 1sigma 范围) 为 0.12 mag。更重要的是，非变化的饱和恒星的光变曲线仅具有中位数离散度为 0.037 mag。DNN 的光变曲线在许多情况下明显优于标准 ASAS-SN 管道所提供的曲线。虽然该网络仅使用 ASAS-SN 的 20 个相机之一的 g 波段数据进行训练，但初步实验表明其也适用于任何相机和早期 ASAS-SN 的 V 波段数据。主要问题似乎与 ASAS-SN 数据降噪管道中关于饱和恒星的可纠正问题有关，而非 DNN 本身。该方法已作为全天自动超新星巡天 Sky Patrol v1.0 上的光变曲线选项公开提供。