Abstract
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.
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Acknowledgments
The authors acknowledge Spanish ministry projects MINE CO AYA2017-89121- P, and support from the European Union’s Horizon 2020 research and innovation program under the H2020-INFRAIA-2018-2020 grant agreement No 210489629. This work has been partially funded by the French National Research Agency (ANR) program APPLY - ANR-19-CE31-0011. This work also benefited from the support of the WOLF project ANR-18-CE31-0018 of the and the OPTICON H2020 (2017–2020) Work Package 1. James Osborn acknowledges support from the UKRI Future Leaders Fellowship (UK) (MR/S035338/1).
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González-Gutiérrez, C., Beltramo-Martin, O., Osborn, J., Calvo-Rolle, J.L., de Cos Juez, F.J. (2020). Atmospheric Tomography Using Convolutional Neural Networks. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_54
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