{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/delight-deep-learning-identification-of","title":"DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multi-resolution Images","arxiv_id":"2208.04310","date":"2022-08-08","proceeding":null,"authors":["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"],"abstract":"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 ($10\\arcsec < r < 60\\arcsec$) and small ($r \\le 10\\arcsec$) 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.","url_abs":"https://arxiv.org/abs/2208.04310v1","url_pdf":"https://arxiv.org/pdf/2208.04310v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"delight-deep-learning-identification-of","repo_url":"https://github.com/fforster/delight","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}