[{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","extern":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1906.09611"}],"type":"preprint","external_id":{"arxiv":["1906.09611"]},"date_published":"2019-06-23T00:00:00Z","year":"2019","citation":{"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>. .","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>.","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>","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>","ista":"Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification of K2 pulsating stars using machine learning techniques. arXiv, 1906.09611.","short":"A.L. Saux, L.A. Bugnet, S. Mathur, S.N. Breton, R.A. Garcia, ArXiv (n.d.).","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>."},"date_updated":"2022-08-22T08:20:29Z","oa":1,"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"}],"day":"23","arxiv":1,"doi":"10.48550/arXiv.1906.09611","keyword":["asteroseismology - methods","data analysis - thecniques","machine learning - stars","oscillations"],"language":[{"iso":"eng"}],"author":[{"last_name":"Saux","first_name":"A. Le","full_name":"Saux, A. Le"},{"last_name":"Bugnet","first_name":"Lisa Annabelle","full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000","id":"d9edb345-f866-11ec-9b37-d119b5234501"},{"last_name":"Mathur","first_name":"S.","full_name":"Mathur, S."},{"full_name":"Breton, S. N.","first_name":"S. N.","last_name":"Breton"},{"first_name":"R. A.","last_name":"Garcia","full_name":"Garcia, R. A."}],"publication":"arXiv","_id":"11630","article_number":"1906.09611","title":"Automatic classification of K2 pulsating stars using machine learning techniques","month":"06","article_processing_charge":"No","date_created":"2022-07-21T06:57:10Z","publication_status":"submitted","oa_version":"Preprint"},{"extern":"1","acknowledgement":"We thank the anonymous referee for the very useful comments. We would also like to thank M. Benbakoura for his help in analyzing the light curves of several binary systems included in our set of stars. L.B. and R.A.G. acknowledge the support from PLATO and GOLF CNES grants. S.M. acknowledges support from the National Aeronautics and Space Administration under Grant NNX15AF13G, the National Science Foundation grant AST-1411685, and the Ramon y Cajal fellowship no. RYC-2015-17697. E.C. is funded by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 664931. O.J.H and B.M.R. acknowledge the support of the UK Science and Technology Facilities Council (STFC). Funding for the Stellar Astrophysics Centre is provided by the Danish National Research Foundation (Grant DNRF106). This research has made use of NASA’s Astrophysics Data System. Data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555.","volume":620,"abstract":[{"lang":"eng","text":"Asteroseismology provides global stellar parameters such as masses, radii, or surface gravities using mean global seismic parameters and effective temperature for thousands of low-mass stars (0.8 M⊙ < M < 3 M⊙). This methodology has been successfully applied to stars in which acoustic modes excited by turbulent convection are measured. Other methods such as the Flicker technique can also be used to determine stellar surface gravities, but only works for log g above 2.5 dex. In this work, we present a new metric called FliPer (Flicker in spectral power density, in opposition to the standard Flicker measurement which is computed in the time domain); it is able to extend the range for which reliable surface gravities can be obtained (0.1 < log g < 4.6 dex) without performing any seismic analysis for stars brighter than Kp < 14. FliPer takes into account the average variability of a star measured in the power density spectrum in a given range of frequencies. However, FliPer values calculated on several ranges of frequency are required to better characterize a star. Using a large set of asteroseismic targets it is possible to calibrate the behavior of surface gravity with FliPer through machine learning. This calibration made with a random forest regressor covers a wide range of surface gravities from main-sequence stars to subgiants and red giants, with very small uncertainties from 0.04 to 0.1 dex. FliPer values can be inserted in automatic global seismic pipelines to either give an estimation of the stellar surface gravity or to assess the quality of the seismic results by detecting any outliers in the obtained νmax values. FliPer also constrains the surface gravities of main-sequence dwarfs using only long-cadence data for which the Nyquist frequency is too low to measure the acoustic-mode properties."}],"doi":"10.1051/0004-6361/201833106","arxiv":1,"day":"01","external_id":{"arxiv":["1809.05105"]},"date_updated":"2022-08-22T07:41:07Z","year":"2018","citation":{"mla":"Bugnet, Lisa Annabelle, et al. “FliPer: A Global Measure of Power Density to Estimate Surface Gravities of Main-Sequence Solar-like Stars and Red Giants.” <i>Astronomy &#38; Astrophysics</i>, vol. 620, A38, EDP Sciences, 2018, doi:<a href=\"https://doi.org/10.1051/0004-6361/201833106\">10.1051/0004-6361/201833106</a>.","short":"L.A. Bugnet, R.A. García, G.R. Davies, S. Mathur, E. Corsaro, O.J. Hall, B.M. Rendle, Astronomy &#38; Astrophysics 620 (2018).","ista":"Bugnet LA, García RA, Davies GR, Mathur S, Corsaro E, Hall OJ, Rendle BM. 2018. FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants. Astronomy &#38; Astrophysics. 620, A38.","apa":"Bugnet, L. A., García, R. A., Davies, G. R., Mathur, S., Corsaro, E., Hall, O. J., &#38; Rendle, B. M. (2018). FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants. <i>Astronomy &#38; Astrophysics</i>. EDP Sciences. <a href=\"https://doi.org/10.1051/0004-6361/201833106\">https://doi.org/10.1051/0004-6361/201833106</a>","ama":"Bugnet LA, García RA, Davies GR, et al. FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants. <i>Astronomy &#38; Astrophysics</i>. 2018;620. doi:<a href=\"https://doi.org/10.1051/0004-6361/201833106\">10.1051/0004-6361/201833106</a>","chicago":"Bugnet, Lisa Annabelle, R. A. García, G. R. Davies, S. Mathur, E. Corsaro, O. J. Hall, and B. M. Rendle. “FliPer: A Global Measure of Power Density to Estimate Surface Gravities of Main-Sequence Solar-like Stars and Red Giants.” <i>Astronomy &#38; Astrophysics</i>. EDP Sciences, 2018. <a href=\"https://doi.org/10.1051/0004-6361/201833106\">https://doi.org/10.1051/0004-6361/201833106</a>.","ieee":"L. A. Bugnet <i>et al.</i>, “FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants,” <i>Astronomy &#38; Astrophysics</i>, vol. 620. EDP Sciences, 2018."},"article_type":"original","publisher":"EDP Sciences","quality_controlled":"1","title":"FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants","intvolume":"       620","publication_status":"published","article_processing_charge":"No","date_created":"2022-07-18T14:37:39Z","author":[{"first_name":"Lisa Annabelle","last_name":"Bugnet","orcid":"0000-0003-0142-4000","full_name":"Bugnet, Lisa Annabelle","id":"d9edb345-f866-11ec-9b37-d119b5234501"},{"last_name":"García","first_name":"R. A.","full_name":"García, R. A."},{"first_name":"G. R.","last_name":"Davies","full_name":"Davies, G. R."},{"first_name":"S.","last_name":"Mathur","full_name":"Mathur, S."},{"last_name":"Corsaro","first_name":"E.","full_name":"Corsaro, E."},{"first_name":"O. J.","last_name":"Hall","full_name":"Hall, O. J."},{"full_name":"Rendle, B. M.","last_name":"Rendle","first_name":"B. M."}],"_id":"11618","scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","main_file_link":[{"url":"https://arxiv.org/abs/1809.05105","open_access":"1"}],"oa":1,"publication_identifier":{"issn":["0004-6361"],"eissn":["1432-0746"]},"date_published":"2018-12-01T00:00:00Z","type":"journal_article","language":[{"iso":"eng"}],"keyword":["Space and Planetary Science","Astronomy and Astrophysics","asteroseismology / methods","data analysis / stars","oscillations"],"month":"12","article_number":"A38","oa_version":"Preprint","publication":"Astronomy & Astrophysics"},{"main_file_link":[{"url":" https://doi.org/10.48550/arXiv.1811.12140","open_access":"1"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","extern":"1","day":"29","doi":"10.48550/arXiv.1811.12140","arxiv":1,"oa":1,"abstract":[{"lang":"eng","text":"The recently launched NASA Transiting Exoplanet Survey Satellite (TESS) mission is going to collect lightcurves for a few hundred million of stars and we expect to increase the number of pulsating stars to analyze compared to the few thousand stars observed by the CoRoT, Kepler and K2 missions. However, most of the TESS targets have not yet been properly classified and characterized. In order to improve the analysis of the TESS data, it is crucial to determine the type of stellar pulsations in a timely manner. We propose an automatic method to classify stars attending to their pulsation properties, in particular, to identify solar-like pulsators among all TESS targets. It relies on the use of the global amount of power contained in the power spectrum (already known as the FliPer method) as a key parameter, along with\r\nthe effective temperature, to feed into a machine learning classifier. Our study, based on TESS simulated datasets, shows that we are able to classify pulsators with a 98% accuracy."}],"citation":{"ieee":"L. A. Bugnet, R. A. García, G. R. Davies, S. Mathur, O. J. Hall, and B. M. Rendle, “FliPer: Classifying TESS pulsating stars,” <i>arXiv</i>. .","chicago":"Bugnet, Lisa Annabelle, R. A. García, G. R. Davies, S. Mathur, O. J. Hall, and B. M. Rendle. “FliPer: Classifying TESS Pulsating Stars.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.1811.12140\">https://doi.org/10.48550/arXiv.1811.12140</a>.","apa":"Bugnet, L. A., García, R. A., Davies, G. R., Mathur, S., Hall, O. J., &#38; Rendle, B. M. (n.d.). FliPer: Classifying TESS pulsating stars. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.1811.12140\">https://doi.org/10.48550/arXiv.1811.12140</a>","ama":"Bugnet LA, García RA, Davies GR, Mathur S, Hall OJ, Rendle BM. FliPer: Classifying TESS pulsating stars. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.1811.12140\">10.48550/arXiv.1811.12140</a>","ista":"Bugnet LA, García RA, Davies GR, Mathur S, Hall OJ, Rendle BM. FliPer: Classifying TESS pulsating stars. arXiv, 1811.12140.","mla":"Bugnet, Lisa Annabelle, et al. “FliPer: Classifying TESS Pulsating Stars.” <i>ArXiv</i>, 1811.12140, doi:<a href=\"https://doi.org/10.48550/arXiv.1811.12140\">10.48550/arXiv.1811.12140</a>.","short":"L.A. Bugnet, R.A. García, G.R. Davies, S. Mathur, O.J. Hall, B.M. Rendle, ArXiv (n.d.)."},"year":"2018","date_updated":"2022-08-22T08:41:55Z","external_id":{"arxiv":["1811.12140"]},"type":"preprint","date_published":"2018-11-29T00:00:00Z","keyword":["asteroseismology - methods","data analysis - stars","oscillations"],"language":[{"iso":"eng"}],"article_processing_charge":"No","date_created":"2022-07-21T07:05:23Z","publication_status":"submitted","oa_version":"Preprint","article_number":"1811.12140","month":"11","title":"FliPer: Classifying TESS pulsating stars","publication":"arXiv","_id":"11631","author":[{"orcid":"0000-0003-0142-4000","full_name":"Bugnet, Lisa Annabelle","first_name":"Lisa Annabelle","last_name":"Bugnet","id":"d9edb345-f866-11ec-9b37-d119b5234501"},{"first_name":"R. A.","last_name":"García","full_name":"García, R. A."},{"full_name":"Davies, G. R.","first_name":"G. R.","last_name":"Davies"},{"first_name":"S.","last_name":"Mathur","full_name":"Mathur, S."},{"full_name":"Hall, O. J.","last_name":"Hall","first_name":"O. J."},{"first_name":"B. M.","last_name":"Rendle","full_name":"Rendle, B. M."}]},{"doi":"10.48550/arXiv.1711.02890","arxiv":1,"day":"08","abstract":[{"lang":"eng","text":"Our understanding of stars through asteroseismic data analysis is limited by our ability to take advantage of the huge amount of observed stars provided by space missions such as CoRoT, Kepler , K2, and soon TESS and PLATO. Global seismic pipelines provide global stellar parameters such as mass and radius using the mean seismic parameters, as well as the effective temperature. These pipelines are commonly used automatically on thousands of stars observed by K2 for 3 months (and soon TESS for at least ∼ 1 month). However, pipelines are not immune from misidentifying noise peaks and stellar oscillations. Therefore, new validation techniques are required to assess the quality of these results. We present a new metric called FliPer (Flicker in Power), which takes into account the average variability at all measured time scales. The proper calibration of FliPer enables us to obtain good estimations of global stellar parameters such as surface gravity that are robust against the influence of noise peaks and hence are an excellent way to find faults in asteroseismic pipelines."}],"oa":1,"date_updated":"2022-08-22T08:45:42Z","year":"2017","citation":{"short":"L.A. Bugnet, R.A. Garcia, G.R. Davies, S. Mathur, E. Corsaro, ArXiv (n.d.).","mla":"Bugnet, Lisa Annabelle, et al. “FliPer: Checking the Reliability of Global Seismic Parameters from Automatic Pipelines.” <i>ArXiv</i>, 1711.02890, doi:<a href=\"https://doi.org/10.48550/arXiv.1711.02890\">10.48550/arXiv.1711.02890</a>.","ista":"Bugnet LA, Garcia RA, Davies GR, Mathur S, Corsaro E. FliPer: Checking the reliability of global seismic parameters from automatic pipelines. arXiv, 1711.02890.","ama":"Bugnet LA, Garcia RA, Davies GR, Mathur S, Corsaro E. FliPer: Checking the reliability of global seismic parameters from automatic pipelines. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.1711.02890\">10.48550/arXiv.1711.02890</a>","apa":"Bugnet, L. A., Garcia, R. A., Davies, G. R., Mathur, S., &#38; Corsaro, E. (n.d.). FliPer: Checking the reliability of global seismic parameters from automatic pipelines. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.1711.02890\">https://doi.org/10.48550/arXiv.1711.02890</a>","chicago":"Bugnet, Lisa Annabelle, R. A. Garcia, G. R. Davies, S. Mathur, and E. Corsaro. “FliPer: Checking the Reliability of Global Seismic Parameters from Automatic Pipelines.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.1711.02890\">https://doi.org/10.48550/arXiv.1711.02890</a>.","ieee":"L. A. Bugnet, R. A. Garcia, G. R. Davies, S. Mathur, and E. Corsaro, “FliPer: Checking the reliability of global seismic parameters from automatic pipelines,” <i>arXiv</i>. ."},"date_published":"2017-11-08T00:00:00Z","external_id":{"arxiv":["1711.02890"]},"type":"preprint","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1711.02890"}],"extern":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","publication_status":"submitted","oa_version":"Preprint","article_processing_charge":"No","date_created":"2022-07-21T07:13:13Z","title":"FliPer: Checking the reliability of global seismic parameters from automatic pipelines","month":"11","article_number":"1711.02890","publication":"arXiv","_id":"11633","author":[{"id":"d9edb345-f866-11ec-9b37-d119b5234501","full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000","last_name":"Bugnet","first_name":"Lisa Annabelle"},{"full_name":"Garcia, R. A.","last_name":"Garcia","first_name":"R. A."},{"first_name":"G. R.","last_name":"Davies","full_name":"Davies, G. R."},{"full_name":"Mathur, S.","first_name":"S.","last_name":"Mathur"},{"full_name":"Corsaro, E.","first_name":"E.","last_name":"Corsaro"}],"language":[{"iso":"eng"}],"keyword":["asteroseismology - methods","data analysis - stars","oscillations"]}]