DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multiresolution Images

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dc.contributor.authorFörster, Francisco
dc.contributor.authorMuñoz Arancibia, Alejandra M.
dc.contributor.authorReyes Jainaga, Ignacio
dc.contributor.authorGagliano, Alexander
dc.contributor.authorBritt, Dylan
dc.contributor.authorCuellar Carrillo, Sara
dc.contributor.authorFigueroa Tapia, Felipe
dc.contributor.authorPolzin, Ava
dc.contributor.authorYousef, Yara
dc.contributor.authorArredondo, Javier
dc.contributor.authorRodríguez Mancini, Diego
dc.contributor.authorCorrea Orellana, Javier
dc.contributor.authorBayo, Amelia
dc.contributor.authorBauer, Franz E.
dc.contributor.authorCatelan, Márcio
dc.contributor.authorCabrera Vives, Guillermo
dc.contributor.authorDastidar, Raya
dc.contributor.authorEstévez, Pablo A.
dc.contributor.authorPignata, Giuliano
dc.contributor.authorHernández García, Lorena
dc.contributor.authorHuijse, Pablo
dc.contributor.authorReyes, Esteban
dc.contributor.authorSánchez Sáez, Paula
dc.contributor.authorRamírez, Mauricio
dc.contributor.authorGrandón, Daniela
dc.contributor.authorPineda García, Jonathan
dc.contributor.authorChabour Barra, Francisca
dc.contributor.authorSilva Farfán, Javier
dc.date.accessioned2023-06-13T17:42:51Z
dc.date.available2023-06-13T17:42:51Z
dc.date.issued2022-11
dc.descriptionIndexación: Scopus.es
dc.description.abstractWe present DELIGHT, or Deep Learning Identification of Galaxy Hosts of Transients, a new algorithm designed to automatically and in real time identify the host galaxies of extragalactic transients. The proposed algorithm receives as input compact, multiresolution images centered at the position of a transient candidate and outputs two-dimensional offset vectors that connect the transient with the center of its predicted host. The multiresolution input consists of a set of images with the same number of pixels, but with progressively larger pixel sizes and fields of view. A sample of 16,791 galaxies visually identified by the Automatic Learning for the Rapid Classification of Events broker team was used to train a convolutional neural network regression model. We show that this method is able to correctly identify both relatively large (10″ < r < 60″) and small (r ≤ 10″) apparent size host galaxies using much less information (32 kB) than with a large, single-resolution image (920 kB). The proposed method has fewer catastrophic errors in recovering the position and is more complete and has less contamination ([removed]es
dc.description.urihttps://iopscience-iop-org.recursosbiblioteca.unab.cl/article/10.3847/1538-3881/ac912a
dc.identifier.citationAstronomical Journal, Volume 164, Issue 51, November 2022, Article number 195es
dc.identifier.doi10.3847/1538-3881/ac912a
dc.identifier.issn0004-6256
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/50632
dc.language.isoenes
dc.publisherAmerican Astronomical Societyes
dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectSupernovaees
dc.subjectGalaxieses
dc.subjectAstroinformaticses
dc.subjectAstronomical object identificationes
dc.subjectClassificationes
dc.titleDELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multiresolution Imageses
dc.typeArtículoes
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