Using artificial neural networks for open-loop tomography

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dc.contributor.authorOsborn, James
dc.contributor.authorDe Cos Juez, Francisco Javier
dc.contributor.authorGuzman, Dani
dc.contributor.authorButterley, Timothy
dc.contributor.authorMyers, Richard
dc.contributor.authorGuesalaga, Andres
dc.contributor.authorLaine, Jesus
dc.date.accessioned2024-01-10T12:10:36Z
dc.date.available2024-01-10T12:10:36Z
dc.date.issued2012
dc.description.abstractModern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide sources that are used to sample the atmosphere. Multi-object adaptive optics (MOAO) is one such technique. Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. We compare our ANN method with a standard least squares type matrix multiplication method and to the learn and apply method developed for the CANARY MOAO instrument. The ANN is trained with a large range of possible turbulent layer positions and therefore does not require any input of the optical turbulence profile. It is therefore less susceptible to changing conditions than some existing methods. We also exploit the non-linear response of the ANN to make it more robust to noisy centroid measurements than other linear techniques. (C) 2012 Optical Society of America
dc.description.funderSchool of Engineering at Pontificia Universidad Catlica de Chile
dc.description.funderEuropean Southern Observatory
dc.description.funderGovernment of Chile
dc.description.funderPontificia Universidad Catolica
dc.description.funderSantander Mobility Grant
dc.description.funderChilean Research Council
dc.description.funderSpanish Science and Innovation Ministry
dc.fechaingreso.objetodigital2024-05-16
dc.format.extent15 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1364/OE.20.002420
dc.identifier.issn1094-4087
dc.identifier.pubmedidMEDLINE:22330480
dc.identifier.urihttps://doi.org/10.1364/OE.20.002420
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/76591
dc.identifier.wosidWOS:000300499500049
dc.information.autorucIngeniería;Guesalaga A ;S/I;63871
dc.information.autorucIngeniería;Guzman D ;S/I;93452
dc.information.autorucIngeniería;Osborn J ;S/I;1009804
dc.issue.numero3
dc.language.isoen
dc.nota.accesocontenido completo
dc.pagina.final2434
dc.pagina.inicio2420
dc.publisherOPTICAL SOC AMER
dc.revistaOPTICS EXPRESS
dc.rightsacceso abierto
dc.subjectWAVE-FRONT RECONSTRUCTION
dc.subjectADAPTIVE OPTICS SYSTEM
dc.subjectFIELD SPECTROSCOPY
dc.subjectFALCON CONCEPT
dc.subjectPERFORMANCE
dc.subject.ods13 Climate Action
dc.subject.odspa13 Acción por el clima
dc.titleUsing artificial neural networks for open-loop tomography
dc.typeartículo
dc.volumen20
sipa.codpersvinculados63871
sipa.codpersvinculados93452
sipa.codpersvinculados1009804
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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