We consider several light curve approximation methods based on neural networks: Multilayer Perceptrons, Bayesian Neural Networks, and Normalizing Flows, to approximate observations of a single light curve. Tests using both the simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey data samples demonstrate that even few observations are enough to fit networks and achieve better approximation quality than other state-of-the-art methods. We show that the methods described in this work have better computational complexity and work faster than Gaussian Processes.
We analyze the performance of the approximation techniques aiming to fill the gaps in the observations of the light curves, and show that the use of appropriate technique increases the accuracy of peak finding and supernova classification. In addition, the study results are organized in a Fulu Python library available on GitHub, which can be easily used by the community. [1,2]
[1]Demianenko M. et al., "Supernova Light Curves Approximation based on Neural Network Models", Proceedings of ACAT-2021 conference, 2022, https://arxiv.org/abs/2206.13306
[2]Demianenko M. et al., "Toward an understanding of the properties of neural network approaches for supernovae light curve approximation", 2022, https://arxiv.org/abs/2209.07542