We use a multilevel perceptron (MLP) neural network to obtain photometry of saturated stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The MLP can obtain fairly unbiased photometry for stars from g~4 to 14~mag, particularly compared to the dispersion (15%-85% 1sigma range around the 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 MLP light curves are, in many cases, spectacularly better than those 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 MLP itself. The method is publicly available as a light curve option on ASAS-SN Sky Patrol v1.0.

翻译：我们采用多层感知器（MLP）神经网络获取全天超新星自动巡天（ASAS-SN）中饱和恒星的测光数据。该MLP能够对星等在g~4至14等的恒星获得相当无偏的测光结果，尤其对于饱和恒星（g<11.5等），其光度弥散度（中值周围15%-85%的1σ范围）为0.12星等。更重要的是，非变光饱和恒星的光变曲线中值弥散度仅为0.037星等。在许多情况下，MLP生成的光变曲线显著优于标准ASAS-SN数据处理流程提供的结果。虽然该网络仅使用ASAS-SN 20个相机中一个相机的g波段数据进行训练，但初步实验表明，该方法可适用于任意相机及早期的ASAS-SN V波段数据。目前存在的主要问题似乎更多源于ASAS-SN饱和恒星数据缩减流程中可修正的缺陷，而非MLP本身。该方法已在ASAS-SN Sky Patrol v1.0平台上作为光变曲线选项公开发布。