| dc.description.abstract | The analysis of astronomical data has made it possible to obtain multiple advances in the
understanding of the universe. In astronomy, for example, light curves allow the stars to be
characterized, allowing them to be known using the available telescopes. stars are naturally
unbalanced by classes which makes automatic recognition difficult as the classification of types
of stars. Currently, statistical models have been proposed for the generation of artificial light
curves, however these models require assumptions that are not necessarily met in the real data
since these models are based on linear relationships that they may not fit non-linear patterns
in the actual data. In this work, the generation of artificial data using adversarial generative
neural networks is proposed. (GAN) using recurrent networks and considering the generation
of time series using bootstrapped sampling of time intervals. The results obtained show that
the model is capable of generating visually and quantitatively more realistic photometric data
than the obtained by state-of-the-art methods based on parametric statistics. It is concluded that
the combination of GAN networks and the bootstrapping method is capable of representing
nonlinear and irregular patterns present in real light curves. As future work, we plan to apply
attention-based networks to select relevant sections in the generation of artificial light curves
using generated and synthetic photometric data. | |
