{'fraction_non_alphanumeric': 0.01653944020356234, 'fraction_numerical': 0.0, 'mean_word_length': 5.450819672131147, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Tomographic reconstructor for multi-object adaptive optics using artificial neural networks

Multi-object adaptive optics requires a tomographic reconstructor to compute the AO correction for scientific targets within the field, using measurements of incoming turbulence from guide stars angularly separated from the science targets. We have developed a reconstructor using an artificial neural network, which is trained in simulation only. We obtained similar or better results than current reconstructors, such as least-squares and Learn and Apply, in simulation and also tested the new technique in the laboratory. The method is robust and can cope well with variations in the atmospheric conditions. We present the technique, our latest results and plans for a full MOAO experiment.