Examinando por Autor "Figueroa Tapia, Felipe"

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    DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multiresolution Images
    (American Astronomical Society, 2022-11) Förster, Francisco; Muñoz Arancibia, Alejandra M.; Reyes Jainaga, Ignacio; Gagliano, Alexander; Britt, Dylan; Cuellar Carrillo, Sara; Figueroa Tapia, Felipe; Polzin, Ava; Yousef, Yara; Arredondo, Javier; Rodríguez Mancini, Diego; Correa Orellana, Javier; Bayo, Amelia; Bauer, Franz E.; Catelan, Márcio; Cabrera Vives, Guillermo; Dastidar, Raya; Estévez, Pablo A.; Pignata, Giuliano; Hernández García, Lorena; Huijse, Pablo; Reyes, Esteban; Sánchez Sáez, Paula; Ramírez, Mauricio; Grandón, Daniela; Pineda García, Jonathan; Chabour Barra, Francisca; Silva Farfán, Javier
    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]