{"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/deep-hits-rotation-invariant-convolutional","title":"Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection","arxiv_id":"1701.00458","date":"2017-01-02","proceeding":null,"authors":["Guillermo Cabrera-Vives","Ignacio Reyes","Francisco Förster","Pablo A. Estévez","Juan-Carlos Maureira"],"abstract":"We introduce Deep-HiTS, a rotation invariant convolutional neural network\n(CNN) model for classifying images of transients candidates into artifacts or\nreal sources for the High cadence Transient Survey (HiTS). CNNs have the\nadvantage of learning the features automatically from the data while achieving\nhigh performance. We compare our CNN model against a feature engineering\napproach using random forests (RF). We show that our CNN significantly\noutperforms the RF model reducing the error by almost half. Furthermore, for a\nfixed number of approximately 2,000 allowed false transient candidates per\nnight we are able to reduce the miss-classified real transients by\napproximately 1/5. To the best of our knowledge, this is the first time CNNs\nhave been used to detect astronomical transient events. Our approach will be\nvery useful when processing images from next generation instruments such as the\nLarge Synoptic Survey Telescope (LSST). We have made all our code and data\navailable to the community for the sake of allowing further developments and\ncomparisons at https://github.com/guille-c/Deep-HiTS.","url_abs":"http://arxiv.org/abs/1701.00458v1","url_pdf":"http://arxiv.org/pdf/1701.00458v1.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":"abstracts"},"code_links":[{"paper_slug":"deep-hits-rotation-invariant-convolutional","repo_url":"https://github.com/guille-c/Deep-HiTS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.00458","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}