{"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/rotation-invariant-convolutional-neural","title":"Rotation-invariant convolutional neural networks for galaxy morphology prediction","arxiv_id":"1503.07077","date":"2015-03-24","proceeding":null,"authors":["Sander Dieleman","Kyle W. Willett","Joni Dambre"],"abstract":"Measuring the morphological parameters of galaxies is a key requirement for\nstudying their formation and evolution. Surveys such as the Sloan Digital Sky\nSurvey (SDSS) have resulted in the availability of very large collections of\nimages, which have permitted population-wide analyses of galaxy morphology.\nMorphological analysis has traditionally been carried out mostly via visual\ninspection by trained experts, which is time-consuming and does not scale to\nlarge ($\\gtrsim10^4$) numbers of images.\n  Although attempts have been made to build automated classification systems,\nthese have not been able to achieve the desired level of accuracy. The Galaxy\nZoo project successfully applied a crowdsourcing strategy, inviting online\nusers to classify images by answering a series of questions. Unfortunately,\neven this approach does not scale well enough to keep up with the increasing\navailability of galaxy images.\n  We present a deep neural network model for galaxy morphology classification\nwhich exploits translational and rotational symmetry. It was developed in the\ncontext of the Galaxy Challenge, an international competition to build the best\nmodel for morphology classification based on annotated images from the Galaxy\nZoo project.\n  For images with high agreement among the Galaxy Zoo participants, our model\nis able to reproduce their consensus with near-perfect accuracy ($> 99\\%$) for\nmost questions. Confident model predictions are highly accurate, which makes\nthe model suitable for filtering large collections of images and forwarding\nchallenging images to experts for manual annotation. This approach greatly\nreduces the experts' workload without affecting accuracy. The application of\nthese algorithms to larger sets of training data will be critical for analysing\nresults from future surveys such as the LSST.","url_abs":"http://arxiv.org/abs/1503.07077v1","url_pdf":"http://arxiv.org/pdf/1503.07077v1.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":"rotation-invariant-convolutional-neural","repo_url":"https://github.com/benanne/kaggle-galaxies","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"rotation-invariant-convolutional-neural","repo_url":"https://github.com/RishabhPatil/GalaxyMorphologyPrediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"morphological-analysis","task_name":"Morphological Analysis"},{"task_slug":"morphology-classification","task_name":"Morphology classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1503.07077","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}