This paper reviews the Stochastic Recurrent Neural Network (SRNN) as applied to the light curves of Active Galactic Nuclei by Sheng et al. (2022). Astronomical data have inherent limitations arising from telescope capabilities, cadence strategies, inevitable observing weather conditions, and current understanding of celestial objects. When applying machine learning methods, it is vital to understand the effects of data limitations on our analysis and ability to make inferences. We take Sheng et al. (2022) as a case study, and illustrate the problems and limitations encountered in implementing the SRNN for simulating AGN variability as seen by the Rubin Observatory.
@article{arxiv.2303.13197,
title = {Stochastic Recurrent Neural Networks for Modelling Astronomical Time Series: Advantages and Limitations},
author = {Xinyue Sheng and Matt Nicholl and Nicholas Ross},
journal= {arXiv preprint arXiv:2303.13197},
year = {2023}
}
Comments
To appear in proceedings of EAS 2022 S11 session on machine learning in astronomy