{"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/star-galaxy-classification-using-deep","title":"Star-galaxy Classification Using Deep Convolutional Neural Networks","arxiv_id":"1608.04369","date":"2016-08-15","proceeding":null,"authors":["Edward J. Kim","Robert J. Brunner"],"abstract":"Most existing star-galaxy classifiers use the reduced summary information\nfrom catalogs, requiring careful feature extraction and selection. The latest\nadvances in machine learning that use deep convolutional neural networks allow\na machine to automatically learn the features directly from data, minimizing\nthe need for input from human experts. We present a star-galaxy classification\nframework that uses deep convolutional neural networks (ConvNets) directly on\nthe reduced, calibrated pixel values. Using data from the Sloan Digital Sky\nSurvey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS),\nwe demonstrate that ConvNets are able to produce accurate and well-calibrated\nprobabilistic classifications that are competitive with conventional machine\nlearning techniques. Future advances in deep learning may bring more success\nwith current and forthcoming photometric surveys, such as the Dark Energy\nSurvey (DES) and the Large Synoptic Survey Telescope (LSST), because deep\nneural networks require very little, manual feature engineering.","url_abs":"http://arxiv.org/abs/1608.04369v2","url_pdf":"http://arxiv.org/pdf/1608.04369v2.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":"star-galaxy-classification-using-deep","repo_url":"https://github.com/EdwardJKim/dl4astro","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.04369","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}