{"doi":"10.48550/arXiv.1906.09611","author":[{"full_name":"Saux, A. Le","first_name":"A. Le","last_name":"Saux"},{"last_name":"Bugnet","id":"d9edb345-f866-11ec-9b37-d119b5234501","first_name":"Lisa Annabelle","full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000"},{"first_name":"S.","full_name":"Mathur, S.","last_name":"Mathur"},{"last_name":"Breton","first_name":"S. N.","full_name":"Breton, S. N."},{"first_name":"R. A.","full_name":"Garcia, R. A.","last_name":"Garcia"}],"_id":"11630","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1906.09611"}],"title":"Automatic classification of K2 pulsating stars using machine learning techniques","publication":"arXiv","publication_status":"submitted","abstract":[{"text":"The second mission of NASA’s Kepler satellite, K2, has collected hundreds of thousands of lightcurves for stars close to the ecliptic plane. This new sample could increase the number of known pulsating stars and then improve our understanding of those stars. For the moment only a few stars have been properly classified and published. In this work, we present a method to automaticly classify K2 pulsating stars using a Machine Learning technique called Random Forest. The objective is to sort out the stars in four classes: red giant (RG), main-sequence Solar-like stars (SL), classical pulsators (PULS) and Other. To do this we use the effective temperatures and the luminosities of the stars as well as the FliPer features, that measures the amount of power contained in the power spectral density. The classifier now retrieves the right classification for more than 80% of the stars.","lang":"eng"}],"article_number":"1906.09611","oa_version":"Preprint","date_updated":"2022-08-22T08:20:29Z","type":"preprint","extern":"1","language":[{"iso":"eng"}],"status":"public","year":"2019","external_id":{"arxiv":["1906.09611"]},"day":"23","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2019-06-23T00:00:00Z","oa":1,"article_processing_charge":"No","citation":{"apa":"Saux, A. L., Bugnet, L. A., Mathur, S., Breton, S. N., &#38; Garcia, R. A. (n.d.). Automatic classification of K2 pulsating stars using machine learning techniques. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.1906.09611\">https://doi.org/10.48550/arXiv.1906.09611</a>","chicago":"Saux, A. Le, Lisa Annabelle Bugnet, S. Mathur, S. N. Breton, and R. A. Garcia. “Automatic Classification of K2 Pulsating Stars Using Machine Learning Techniques.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.1906.09611\">https://doi.org/10.48550/arXiv.1906.09611</a>.","ista":"Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification of K2 pulsating stars using machine learning techniques. arXiv, 1906.09611.","ieee":"A. L. Saux, L. A. Bugnet, S. Mathur, S. N. Breton, and R. A. Garcia, “Automatic classification of K2 pulsating stars using machine learning techniques,” <i>arXiv</i>. .","short":"A.L. Saux, L.A. Bugnet, S. Mathur, S.N. Breton, R.A. Garcia, ArXiv (n.d.).","ama":"Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification of K2 pulsating stars using machine learning techniques. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.1906.09611\">10.48550/arXiv.1906.09611</a>","mla":"Saux, A. Le, et al. “Automatic Classification of K2 Pulsating Stars Using Machine Learning Techniques.” <i>ArXiv</i>, 1906.09611, doi:<a href=\"https://doi.org/10.48550/arXiv.1906.09611\">10.48550/arXiv.1906.09611</a>."},"keyword":["asteroseismology - methods","data analysis - thecniques","machine learning - stars","oscillations"],"date_created":"2022-07-21T06:57:10Z","month":"06"}