http://arxiv.org/abs/2303.13197
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.
X. Sheng, M. Nicholl and N. Ross
Fri, 24 Mar 23
19/56
Comments: To appear in proceedings of EAS 2022 S11 session on machine learning in astronomy