Convolutional CARMENtomographic Reconstruction for Night Observation

  1. Francisco García Riesgo 11
  2. Sergio Luis Suárez Gómez 11
  3. Fernando Sánchez Lasheras 11
  4. Carlos González Gutiérrez 11
  5. Carmen Peñalver San Cristóbal 1
  6. Cos Juez, Francisco Javier de 11
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Libro:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Año de publicación: 2019

Páginas: 335-345

Congreso: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipo: Aportación congreso

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Resumen

To remove the distortion that the atmosphere causes in the observations performed with extremely large telescopes, correction techniques are required. To tackle this problem, adaptive optics systems uses wave front sensors obtain measures of the atmospheric turbulence and hence, estimate a reconstruction of the atmosphere when this calculation is applied in deformable mirrors, which compensates the aberrated wave front. In Multi Object Adaptive Optics (MOAO), several Shack-Hartmann wave front sensors along with reference guide stars are used to characterize the aberration produced by the atmosphere. Typically, this is a two-step process, where a centroiding algorithm is applied to the image provided by the sensor and the centroids from different Shack-Hartmanns wave front sensors are combined by using a Least Squares algorithm or an Artificial Neural Network, such as the Multi-Layer Perceptron. In this article a new solution based on Convolutional Neural Networks is proposed, which allows to integrate both the centroiding and the tomographic reconstruction in the same algorithm, getting a substantial improvement over the traditional Least Squares algorithm and a similar performance than the Multi-Layer Perceptron, but without the need of previously computing the centroiding algorithm.

Fuente de los datos: Dialnet