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

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dc.catalogadoryvc
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.accessioned2024-02-28T13:37:37Z
dc.date.available2024-02-28T13:37:37Z
dc.date.issued2022
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 (<0.86%) recovering the crossmatched redshift than other state-of-the-art methods. The more efficient representation provided by multiresolution input images could allow for the identification of transient host galaxies in real time, if adopted in alert streams from new generation of large -etendue telescopes such as the Vera C. Rubin Observatory....
dc.fechaingreso.objetodigital2024-05-29
dc.fuente.origenORCID
dc.identifier.doi10.3847/1538-3881/ac912a
dc.identifier.urihttps://doi.org/10.3847/1538-3881/ac912a
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/81390
dc.information.autorucInstituto de Astrofísica ; Catelan, Márcio ; 0000-0001-6003-8877 ; 1001556
dc.language.isoen
dc.nota.accesoContenido completo
dc.rightsacceso abierto
dc.titleDELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multiresolution Images
dc.typeartículo
sipa.codpersvinculados1001556
sipa.trazabilidadORCID;2024-01-22
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