Analysing the Performance of a Tomographic Reconstructor with Different Neural Networks Frameworks

• Published in: Intelligent Systems Design and Applications Vol. 557; pp. 1051 - 1060
• Main Authors: Gómez, Sergio Luis Suárez, Gutiérrez, Carlos González, Rodríguez, Jesús Daniel Santos, Rodríguez, María Luisa Sánchez, Lasheras, Fernando Sánchez, de Cos Juez, Francisco Javier
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Advances in Intelligent Systems and Computing
• Subjects: Adaptive optics; Artificial intelligence; Neural networks; Theano; Torch
• ISBN: 9783319534794; 3319534793
• ISSN: 2194-5357; 2194-5365
• DOI: 10.1007/978-3-319-53480-0_103

Correction of atmospheric turbulences with the use of guide stars as reference, is one of the most relevant issues of adaptive optics (AO). This is addressed with tomographic techniques such as Multi-object adaptive optics (MOAO). Next generations of extremely large telescopes, will require improvements in computational capabilities of real time control systems. An improved version of CARMEN, a tomographic reconstructor based on machine learning, is presented here. The performing time of two dedicated neural network frameworks, Torch and Theano, is compared, with significant improvements on the training and execution times of the neural networks due to calculations on GPU. Also, the differences between both frameworks are discussed.