Photometry of Saturated Stars with Neural Networks
<|reference_start|>Photometry of Saturated Stars with Neural Networks: 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.<|reference_end|>
@article{winecki2024photometry,
    title={Photometry of Saturated Stars with Neural Networks},
    author={Dominik Winecki (1) Christopher S. Kochanek (2) ((1) Dept. of Computer
  Science and Engineeering, The Ohio State University (2) Dept. of Astronomy,
  The Ohio State University)},
    journal={arXiv preprint arXiv:2404.15405},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15405},
    primaryClass={astro-ph.SR astro-ph.IM cs.CV}
}