Resumen de Atmospheric Tomography Using Convolutional Neural Networks

Carlos González Gutiérrez, Olivier Beltramo Martin, J. Osborn, José Luis Calvo Rolle, Francisco Javier de Cos Juez

• We present an application of Convolutional Neural Networks (CNN) to atmospheric tomography that is required for compensating optical aberrations introduced by the atmospheric turbulence using dedicated tomographic Adaptive Optics (AO) systems.We compare the state of the art Minimum Mean Square Error (MMSE) reconstructor with a Multi-Layer Perceptron (MLP) and a CNN architecture and show that the CNN performs up to 15%–20% better than the MMSE and is more robust to atmospheric profile variations up to 10% compared to the MLP. Such results pave the way to implement CNN architectures to revisit atmospheric tomography for astronomical telescopes equipped with AO.