Open-loop tomography with artificial neural networks on CANARY: on-sky results

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dc.contributor.authorOsborn, J.
dc.contributor.authorGuzman, D.
dc.contributor.authorde Cos Juez, F. J.
dc.contributor.authorBasden, A. G.
dc.contributor.authorMorris, T. J.
dc.contributor.authorGendron, E.
dc.contributor.authorButterley, T.
dc.contributor.authorMyers, R. M.
dc.contributor.authorGuesalaga, A.
dc.contributor.authorSanchez Lasheras, F.
dc.contributor.authorGomez Victoria, M.
dc.contributor.authorSanchez Rodriguez, M. L.
dc.contributor.authorGratadour, D.
dc.contributor.authorRousset, G.
dc.date.accessioned2025-01-23T21:44:45Z
dc.date.available2025-01-23T21:44:45Z
dc.date.issued2014
dc.description.abstractWe present recent results from the initial testing of an artificial neural network (ANN)-based tomographic reconstructor Complex Atmospheric Reconstructor based on Machine lEarNing (CARMEN) on CANARY, an adaptive optics demonstrator operated on the 4.2 m William Herschel Telescope, La Palma. The reconstructor was compared with contemporaneous data using the Learn and Apply (L&A) tomographic reconstructor. We find that the fully optimized L&A tomographic reconstructor outperforms CARMEN by approximately 5 per cent in Strehl ratio or 15 nm rms in wavefront error. We also present results for CANARY in Ground Layer Adaptive Optics mode to show that the reconstructors are tomographic. The results are comparable and this small deficit is attributed to limitations in the training data used to build the ANN. Laboratory bench tests show that the ANN can outperform L&A under certain conditions, e.g. if the higher layer of a model two layer atmosphere was to change in altitude by similar to 300 m (equivalent to a shift of approximately one tenth of a subaperture).
dc.fuente.origenWOS
dc.identifier.doi10.1093/mnras/stu758
dc.identifier.eissn1365-2966
dc.identifier.issn0035-8711
dc.identifier.urihttps://doi.org/10.1093/mnras/stu758
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/101699
dc.identifier.wosidWOS:000338763600055
dc.issue.numero3
dc.language.isoen
dc.pagina.final2514
dc.pagina.inicio2508
dc.revistaMonthly notices of the royal astronomical society
dc.rightsacceso restringido
dc.subjectatmospheric effects
dc.subjectinstrumentation: adaptive optics
dc.subject.ods13 Climate Action
dc.subject.odspa13 Acción por el clima
dc.titleOpen-loop tomography with artificial neural networks on CANARY: on-sky results
dc.typeartículo
dc.volumen441
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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