Photometry of Saturated Stars with Neural Networks

TL;DR

This paper introduces a neural network approach to accurately measure the brightness of saturated stars in the ASAS-SN survey, significantly improving photometry quality over standard pipelines.

Contribution

The study presents a novel neural network method for saturated star photometry that outperforms existing pipelines and is adaptable across different cameras and bands.

Findings

Median dispersion of 0.037 mag for non-variable stars

Unbiased photometry for stars g~4 to 14 mag

Method applicable to multiple cameras and bands

Abstract

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.