Photometry of Saturated Stars with Neural Networks

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% 1 σ range around the median) of 0.12 mag for saturated ( g < 11.5 mag) stars. More importantly, the light curve of a nonvariable 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.

Keywords

• CCD photometry
• Time domain astronomy
• Algol variable stars
• Computational methods