González-Gutiérrez, Carlos (University of Oviedo), Férnández Valdivia, Juan José (Wooptix), Suárez Gómez, Sergio Luis (University of Oviedo), Rodríguez Ramos, José Manuel (Wooptix), Rodríguez Ramos, Luis Fernando (Instituto de Astrofísica de Canarias), De Cos Juez, Francisco Javier (University of Oviedo)
Astronomical images taken from large ground-based telescopes requires techniques as Adaptive Optics in order to improve their spatial resolution. In this work are presented computational results from a modified curvature sensor, the Tomographic Pupil Image Wavefront Sensor (TPI-WFS), which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional Neural Networks (CNN) are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of Artificial Neural Networks (ANN), which are widely known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements (Root Mean Square error, Mean Structural Similarity and Strehl ratio). In general, CNN trained as reconstructor showed slightly better performance than the conventional reconstruction in TPI-WFS for most of the turbulent profiles, but it made significant improvements for higher turbulent profiles that have the lowest r0 values.
10.26698/AO4ELT5.0040- Proceeding PDF