DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multiresolution Images
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Archivos
Fecha
2022-11
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
American Astronomical Society
Nombre de Curso
Licencia CC
Atribución 4.0 Internacional (CC BY 4.0)
Licencia CC
https://creativecommons.org/licenses/by/4.0/deed.es
Resumen
We 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]
Notas
Indexación: Scopus.
Palabras clave
Supernovae, Galaxies, Astroinformatics, Astronomical object identification, Classification
Citación
Astronomical Journal, Volume 164, Issue 51, November 2022, Article number 195
DOI
10.3847/1538-3881/ac912a