Astronomy and Astrophysics
Aims: Our aim here is to propose a general unsupervised training scheme that allows multiframe blind deconvolution deep learning systems to be trained with observations only. The approach can be applied for the correction of point-like as well as extended objects.
Methods: Leveraging the linear image formation theory and a probabilistic approach to the blind deconvolution problem produces a physically motivated loss function. Optimization of this loss function allows end-to-end training of a machine learning model composed of three neural networks.
Results: As examples, we apply this procedure to the deconvolution of stellar data from the FastCam instrument and to solar extended data from the Swedish Solar Telescope. The analysis demonstrates that the proposed neural model can be successfully trained without supervision using observations only. It provides estimations of the instantaneous wavefronts, from which a corrected image can be found using standard deconvolution techniques. The network model is roughly three orders of magnitude faster than applying standard deconvolution based on optimization and shows potential to be used on real-time at the telescope.
Los campos magnéticos son uno de los ingredientes fundamentales en la formación de estrellas y su evolución. En el nacimiento de una estrella, los campos magnéticos llegan a frenar su rotación durante el colapso de la nube molecular, y en el fin de la vida de una estrella, el magnetismo puede ser clave en la forma en la que se pierden las capas
Los campos magnéticos están presentes en todos los plasmas astrofísicos y controlan la mayor parte de la variabilidad que se observa en el Universo a escalas temporales intermedias. Se encuentran en estrellas, a lo largo de todo el diagrama de Hertzsprung-Russell, en galaxias, e incluso quizás en el medio intergaláctico. La polarización de la luz