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Using GPUs to Speed up a Tomographic Reconstructor Based on Machine Learning

  • Carlos González-Gutiérrez
  • Jesús Daniel Santos-RodríguezEmail author
  • Ramón Ángel Fernández Díaz
  • Jose Luis Calvo Rolle
  • Nieves Roqueñí Gutiérrez
  • Francisco Javier de Cos Juez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)

Abstract

The next generation of adaptive optics (AO) systems require tomographic techniques in order to correct for atmospheric turbulence along lines of sight separated from the guide stars. Multi-object adaptive optics (MOAO) is one such technique. Here we present an improved version of CARMEN, a tomographic reconstructor based on machine learning, using a dedicated neural network framework as Torch. We can observe a significant improvement on the training an execution times of the neural network, thanks to the use of the GPU.

Keywords

Neural networks Torch Adaptive optics 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Carlos González-Gutiérrez
    • 1
  • Jesús Daniel Santos-Rodríguez
    • 2
    Email author
  • Ramón Ángel Fernández Díaz
    • 3
  • Jose Luis Calvo Rolle
    • 4
  • Nieves Roqueñí Gutiérrez
    • 1
  • Francisco Javier de Cos Juez
    • 1
  1. 1.Department of Exploitation and Exploration of MinesUniversity of OviedoOviedoSpain
  2. 2.Department of PhysicsUniversity of OviedoOviedoSpain
  3. 3.Department of Architecture and Technology of ComputersUniversity of LeónLeónSpain
  4. 4.Department of Industrial EngineeringUniversity of a CoruñaLa CoruñaSpain

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