Papers › DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using...
DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multi-resolution Images
Francisco Förster, Alejandra M. Muñoz Arancibia, Ignacio Reyes, Alexander Gagliano, Dylan Britt, Sara Cuellar-Carrillo, Felipe Figueroa-Tapia, Ava Polzin, Yara Yousef, Javier Arredondo, Diego Rodríguez-Mancini, Javier Correa-Orellana, Amelia Bayo, Franz E. Bauer, Márcio Catelan, Guillermo Cabrera-Vives, Raya Dastidar, Pablo A. Estévez, Giuliano Pignata, Lorena Hernandez-Garcia, Pablo Huijse, Esteban Reyes, Paula Sánchez-Sáez, Mauricio Ramirez, Daniela Grandón, Jonathan Pineda-García, Francisca Chabour-Barra, Javier Silva-Farfán
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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, multi-resolution 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 multi-resolution 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 \nSample galaxies visually identified by the ALeRCE 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 cross-matched redshift than other state-of-the-art methods. The more efficient representation provided by multi-resolution 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.
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