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.
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González-Gutiérrez, C., Santos-Rodríguez, J.D., Díaz, R.Á.F., Rolle, J.L.C., Gutiérrez, N.R., de Cos Juez, F.J. (2017). Using GPUs to Speed up a Tomographic Reconstructor Based on Machine Learning. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_27
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DOI: https://doi.org/10.1007/978-3-319-47364-2_27
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