Atmospheric Tomography Using Convolutional Neural Networks

  1. C. González-Gutiérrez 15
  2. O. Beltramo-Martin 2
  3. J. Osborn 3
  4. José Luis Calvo-Rolle 4
  5. Cos Juez, F.J. de 15
  1. 1 Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (Oviedo)
  2. 2 Aix-Marseille University
    info

    Aix-Marseille University

    Marsella, Francia

    ROR https://ror.org/035xkbk20

  3. 3 Durham University
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    Durham University

    Durham, Reino Unido

    ROR https://ror.org/01v29qb04

  4. 4 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  5. 5 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

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Libro:
Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings
  1. Cesar Analide (ed. lit.)
  2. Paulo Novais (ed. lit.)
  3. David Camacho (ed. lit.)
  4. Hujun Yin (ed. lit.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-62362-3,  978-3-030-62361-6,  978-3-030-62364-7,  978-3-030-62365-4

Año de publicación: 2020

Título del volumen: Part II

Volumen: 2

Páginas: 561-569

Congreso: Intelligent Data Engineering and Automated Learning – IDEAL (21. 2020. Guimarães)

Tipo: Aportación congreso

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

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.

Fuente de los datos: Dialnet