{"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-learnt-classification-of-light-curves","title":"Deep-Learnt Classification of Light Curves","arxiv_id":"1709.06257","date":"2017-09-19","proceeding":null,"authors":["Ashish Mahabal","Kshiteej Sheth","Fabian Gieseke","Akshay Pai","S. George Djorgovski","Andrew Drake","Matthew Graham","the CSS/CRTS/PTF Collaboration"],"abstract":"Astronomy light curves are sparse, gappy, and heteroscedastic. As a result\nstandard time series methods regularly used for financial and similar datasets\nare of little help and astronomers are usually left to their own instruments\nand techniques to classify light curves. A common approach is to derive\nstatistical features from the time series and to use machine learning methods,\ngenerally supervised, to separate objects into a few of the standard classes.\nIn this work, we transform the time series to two-dimensional light curve\nrepresentations in order to classify them using modern deep learning\ntechniques. In particular, we show that convolutional neural networks based\nclassifiers work well for broad characterization and classification. We use\nlabeled datasets of periodic variables from CRTS survey and show how this opens\ndoors for a quick classification of diverse classes with several possible\nexciting extensions.","url_abs":"http://arxiv.org/abs/1709.06257v1","url_pdf":"http://arxiv.org/pdf/1709.06257v1.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-learnt-classification-of-light-curves","repo_url":"https://github.com/hombit/light-curve","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deep-learnt-classification-of-light-curves","repo_url":"https://github.com/light-curve/light-curve","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deep-learnt-classification-of-light-curves","repo_url":"https://github.com/sakshambassi/DeepStarClassification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"astronomy","task_name":"Astronomy"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1709.06257","atlas_url":"https://app.syntology.ai/?focus=1709.06257","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}