Restoration of T80-S telescope's images using neural networks

Simple item page
dc.contributor.authorBernardi, Rafael L.
dc.contributor.authorBerdja, Amokrane
dc.contributor.authorGuzman, Christian Dani
dc.contributor.authorTorres-Torriti, Miguel
dc.contributor.authorRoth, Martin M.
dc.date.accessioned2025-01-20T20:07:25Z
dc.date.available2025-01-20T20:07:25Z
dc.date.issued2023
dc.description.abstractConvolutional neural networks (CNNs) have been used for a wide range of applications in astronomy, including for the restoration of degraded images using a spatially invariant point spread function (PSF) across the field of view. Most existing development techniques use a single PSF in the deconvolution process, which is unrealistic when spatially variable PSFs are present in real observation conditions. Such conditions are simulated in this work to yield more realistic data samples. We propose a method that uses a simulated spatially variable PSF for the T80-South (T80-S) telescope, an 80-cm survey imager at Cerro Tololo (Chile). The synthetic data use real parameters from the detector noise and atmospheric seeing to recreate the T80-S observational conditions for the CNN training. The method is tested on real astronomical data from the T80-S telescope. We present the simulation and training methods, the results from real T80-S image CNN prediction, and a comparison with space observatory Gaia. A CNN can fix optical aberrations, which include image distortion, PSF size and profile, and the field position variation while preserving the source's flux. The proposed restoration approach can be applied to other optical systems and to post-process adaptive optics static residual aberrations in large-diameter telescopes.
dc.fuente.origenWOS
dc.identifier.doi10.1093/mnras/stad2050
dc.identifier.eissn1365-2966
dc.identifier.issn0035-8711
dc.identifier.urihttps://doi.org/10.1093/mnras/stad2050
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91799
dc.identifier.wosidWOS:001048625100021
dc.issue.numero2
dc.language.isoen
dc.pagina.final3082
dc.pagina.inicio3068
dc.revistaMonthly notices of the royal astronomical society
dc.rightsacceso restringido
dc.subjectmethods: statistical
dc.subjecttechniques: image processing
dc.subjectsoftware: data analysis
dc.subject.ods13 Climate Action
dc.subject.odspa13 Acción por el clima
dc.titleRestoration of T80-S telescope's images using neural networks
dc.typeartículo
dc.volumen524
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Collections
Artículos de revistas