Photometry of Saturated Stars with Neural Networks
This provides improved light curves for astronomers studying saturated stars, though it is incremental as it builds on existing neural network methods applied to a specific data issue.
The researchers tackled the problem of obtaining accurate photometry for saturated stars in the ASAS-SN survey by using a multilevel perceptron neural network, achieving a median dispersion of 0.037 mag for non-variable saturated stars, which is significantly better than standard pipelines.
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.