DEEPLOOP: DEEP Learning for an Optimized adaptive Optics Psf estimation

Morgan Gray; Maxime Dumont; Olivier Beltramo-Martin; Jean-Charles Lambert; Benoit Neichel; Thierry Fusco

doi:10.1117/12.2629874

29 August 2022 DEEPLOOP: DEEP Learning for an Optimized adaptive Optics Psf estimation

Morgan Gray, Maxime Dumont, Olivier Beltramo-Martin, Jean-Charles Lambert, Benoit Neichel, Thierry Fusco

Proceedings Volume 12185, Adaptive Optics Systems VIII; 1218538 (2022) https://doi.org/10.1117/12.2629874
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada

Conference Poster

Abstract

DEEPLOOP is a Python toolbox, originally dedicated to the estimation of the parameters of an Adaptive Optics (AO) Point Spread Function (PSF), describing the atmospheric turbulence and the static modes of a telescope. This toolbox is using the Tensorflow/Keras deep learning API and a Graphical Processor Unit (GPU) computing framework. DEEPLOOP is based on a small set of Python scripts dedicated to the data loading, to the Neural Network (NN) models architectures and their compiling, to the training methods, to the learning curves display and to the performances evaluation on the test sets. This toolbox has a great flexibility: it enables to make simulations on a specific parameters grid (for searching the best hyperparameters configuration), to parallelize the calculations on several GPUs (synchronous data parallelism on the same node), and to use some specific ’on-the-fly’ images loading for each batch, in order to use very few Random Access Memory (RAM). In this paper, we will first explain the main characteristics of this toolbox. Then, the first results with data simulations on Keck II telescope will be presented.

Citation Download Citation

Morgan Gray, Maxime Dumont, Olivier Beltramo-Martin, Jean-Charles Lambert, Benoit Neichel, and Thierry Fusco "DEEPLOOP: DEEP Learning for an Optimized adaptive Optics Psf estimation", Proc. SPIE 12185, Adaptive Optics Systems VIII, 1218538 (29 August 2022); https://doi.org/10.1117/12.2629874

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