Title: Photometry of Saturated Stars with Machine Learning

Abstract: 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.