{"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/fundamental-parameters-of-main-sequence-stars","title":"Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning","arxiv_id":"1607.02137","date":"2016-07-06","proceeding":null,"authors":["Earl P. Bellinger","George C. Angelou","Saskia Hekker","Sarbani Basu","Warrick Ball","Elisabeth Guggenberger"],"abstract":"Owing to the remarkable photometric precision of space observatories like\nKepler, stellar and planetary systems beyond our own are now being\ncharacterized en masse for the first time. These characterizations are pivotal\nfor endeavors such as searching for Earth-like planets and solar twins,\nunderstanding the mechanisms that govern stellar evolution, and tracing the\ndynamics of our Galaxy. The volume of data that is becoming available, however,\nbrings with it the need to process this information accurately and rapidly.\nWhile existing methods can constrain fundamental stellar parameters such as\nages, masses, and radii from these observations, they require substantial\ncomputational efforts to do so.\n  We develop a method based on machine learning for rapidly estimating\nfundamental parameters of main-sequence solar-like stars from classical and\nasteroseismic observations. We first demonstrate this method on a\nhare-and-hound exercise and then apply it to the Sun, 16 Cyg A & B, and 34\nplanet-hosting candidates that have been observed by the Kepler spacecraft. We\nfind that our estimates and their associated uncertainties are comparable to\nthe results of other methods, but with the additional benefit of being able to\nexplore many more stellar parameters while using much less computation time. We\nfurthermore use this method to present evidence for an empirical diffusion-mass\nrelation. Our method is open source and freely available for the community to\nuse.\n  The source code for all analyses and for all figures appearing in this\nmanuscript can be found electronically at\nhttps://github.com/earlbellinger/asteroseismology","url_abs":"http://arxiv.org/abs/1607.02137v1","url_pdf":"http://arxiv.org/pdf/1607.02137v1.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":"fundamental-parameters-of-main-sequence-stars","repo_url":"https://github.com/earlbellinger/asteroseismology","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}