| |
 |
| Paper: |
Astronomical Data Approximation Based on Neural Network Models |
| Volume: |
535, Astronomical Data Analysis Software and Systems XXXI |
| Page: |
131 |
| Authors: |
Samorodova, E.; Demianenko, M.; Sysak, M.; Shiriaev, A.; Malanchev, K.; Derkach, D.; Hushchyn, M. |
| Abstract: |
In this study, we apply shallow neural networks, bayesian neural networks, and normalizing flows to approximate light curves of astronomical objects. The study shows that the approximation quality of the proposed methods outperforms the existing approaches based on Gaussian processes. We assess the quality of solution using two physics-motivated analyses: supernovae type Ia classification and bolometric intensity peak estimation. For both problems, convolutional neural networks are trained on approximated light curves. The results show that the proposed methods help to improve the quality of supernovae type identification and increase the accuracy of the intensity peak estimation compared to the Gaussian processes model.
|
|
|
|
|
|
