Generacion de datos fotométricos artificiales de estrellas variables con Boostrapped GAN

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dc.contributor.advisorPeralta, Billy
dc.contributor.advisorNicolis, Orietta
dc.contributor.authorVergara Sepúlveda, Álvaro
dc.contributor.editorFacultad de Ingeniería
dc.date.accessioned2023-01-17T12:07:33Z
dc.date.available2023-01-17T12:07:33Z
dc.date.issued2022
dc.descriptionTesis (Magíster en Ciencias de la Computación)es
dc.description.abstractThe 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.es
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/36160
dc.language.isoeses
dc.publisherUniversidad Andrés Belloes
dc.titleGeneracion de datos fotométricos artificiales de estrellas variables con Boostrapped GANes
dc.typeTesises
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