[{"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"https://arxiv.org/abs/2101.10152","open_access":"1"}],"oa":1,"publication_identifier":{"issn":["0004-6361"],"eissn":["1432-0746"]},"type":"journal_article","date_published":"2021-03-19T00:00:00Z","keyword":["Space and Planetary Science","Astronomy and Astrophysics","methods: data analysis / stars: solar-type / stars: activity / stars: rotation / starspots"],"language":[{"iso":"eng"}],"article_number":"A125","month":"03","oa_version":"Preprint","publication":"Astronomy & Astrophysics","extern":"1","acknowledgement":"We thank Suzanne Aigrain and Joe Llama for providing us with the simulated data used in Aigrain et al. (2015). S. N. B., L. B. and R. A. G. acknowledge the support from PLATO and GOLF CNES grants. A. R. G. S. acknowledges the support from NASA under grant NNX17AF27G. S. M. acknowledges the support from the Spanish Ministry of Science and Innovation with the Ramon y Cajal fellowship number RYC-2015-17697. P. L. P. and S. M. acknowledge support from the Spanish Ministry of Science and Innovation with the grant number PID2019-107187GB-I00. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. Software: Python (Van Rossum & Drake 2009), numpy (Oliphant 2006), pandas (The pandas development team 2020; McKinney 2010), matplotlib (Hunter 2007), scikit-learn (Pedregosa et al. 2011). The source code used to obtain the present results can be found at: https://gitlab.com/sybreton/pushkin ; https://gitlab.com/sybreton/ml_surface_rotation_paper .","volume":647,"abstract":[{"text":"In order to understand stellar evolution, it is crucial to efficiently determine stellar surface rotation periods. Indeed, while they are of great importance in stellar models, angular momentum transport processes inside stars are still poorly understood today. Surface rotation, which is linked to the age of the star, is one of the constraints needed to improve the way those processes are modelled. Statistics of the surface rotation periods for a large sample of stars of different spectral types are thus necessary. An efficient tool to automatically determine reliable rotation periods is needed when dealing with large samples of stellar photometric datasets. The objective of this work is to develop such a tool. For this purpose, machine learning classifiers constitute relevant bases to build our new methodology. Random forest learning abilities are exploited to automate the extraction of rotation periods in Kepler light curves. Rotation periods and complementary parameters are obtained via three different methods: a wavelet analysis, the autocorrelation function of the light curve, and the composite spectrum. We trained three different classifiers: one to detect if rotational modulations are present in the light curve, one to flag close binary or classical pulsators candidates that can bias our rotation period determination, and finally one classifier to provide the final rotation period. We tested our machine learning pipeline on 23 431 stars of the Kepler K and M dwarf reference rotation catalogue for which 60% of the stars have been visually inspected. For the sample of 21 707 stars where all the input parameters are provided to the algorithm, 94.2% of them are correctly classified (as rotating or not). Among the stars that have a rotation period in the reference catalogue, the machine learning provides a period that agrees within 10% of the reference value for 95.3% of the stars. Moreover, the yield of correct rotation periods is raised to 99.5% after visually inspecting 25.2% of the stars. Over the two main analysis steps, rotation classification and period selection, the pipeline yields a global agreement with the reference values of 92.1% and 96.9% before and after visual inspection. Random forest classifiers are efficient tools to determine reliable rotation periods in large samples of stars. The methodology presented here could be easily adapted to extract surface rotation periods for stars with different spectral types or observed by other instruments such as K2, TESS or by PLATO in the near future.","lang":"eng"}],"day":"19","doi":"10.1051/0004-6361/202039947","arxiv":1,"external_id":{"arxiv":["2101.10152"]},"citation":{"ista":"Breton SN, Santos ARG, Bugnet LA, Mathur S, García RA, Pallé PL. 2021. ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods. Astronomy &#38; Astrophysics. 647, A125.","short":"S.N. Breton, A.R.G. Santos, L.A. Bugnet, S. Mathur, R.A. García, P.L. Pallé, Astronomy &#38; Astrophysics 647 (2021).","mla":"Breton, S. N., et al. “ROOSTER: A Machine-Learning Analysis Tool for Kepler Stellar Rotation Periods.” <i>Astronomy &#38; Astrophysics</i>, vol. 647, A125, EDP Sciences, 2021, doi:<a href=\"https://doi.org/10.1051/0004-6361/202039947\">10.1051/0004-6361/202039947</a>.","ieee":"S. N. Breton, A. R. G. Santos, L. A. Bugnet, S. Mathur, R. A. García, and P. L. Pallé, “ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods,” <i>Astronomy &#38; Astrophysics</i>, vol. 647. EDP Sciences, 2021.","chicago":"Breton, S. N., A. R. G. Santos, Lisa Annabelle Bugnet, S. Mathur, R. A. García, and P. L. Pallé. “ROOSTER: A Machine-Learning Analysis Tool for Kepler Stellar Rotation Periods.” <i>Astronomy &#38; Astrophysics</i>. EDP Sciences, 2021. <a href=\"https://doi.org/10.1051/0004-6361/202039947\">https://doi.org/10.1051/0004-6361/202039947</a>.","apa":"Breton, S. N., Santos, A. R. G., Bugnet, L. A., Mathur, S., García, R. A., &#38; Pallé, P. L. (2021). ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods. <i>Astronomy &#38; Astrophysics</i>. EDP Sciences. <a href=\"https://doi.org/10.1051/0004-6361/202039947\">https://doi.org/10.1051/0004-6361/202039947</a>","ama":"Breton SN, Santos ARG, Bugnet LA, Mathur S, García RA, Pallé PL. ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods. <i>Astronomy &#38; Astrophysics</i>. 2021;647. doi:<a href=\"https://doi.org/10.1051/0004-6361/202039947\">10.1051/0004-6361/202039947</a>"},"year":"2021","date_updated":"2022-08-22T08:47:47Z","article_type":"original","publisher":"EDP Sciences","quality_controlled":"1","intvolume":"       647","title":"ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods","date_created":"2022-07-18T12:21:32Z","article_processing_charge":"No","publication_status":"published","author":[{"first_name":"S. N.","last_name":"Breton","full_name":"Breton, S. N."},{"first_name":"A. R. G.","last_name":"Santos","full_name":"Santos, A. R. G."},{"last_name":"Bugnet","first_name":"Lisa Annabelle","full_name":"Bugnet, Lisa Annabelle","orcid":"0000-0003-0142-4000","id":"d9edb345-f866-11ec-9b37-d119b5234501"},{"first_name":"S.","last_name":"Mathur","full_name":"Mathur, S."},{"full_name":"García, R. A.","first_name":"R. A.","last_name":"García"},{"first_name":"P. L.","last_name":"Pallé","full_name":"Pallé, P. L."}],"scopus_import":"1","_id":"11608"},{"publisher":"EDP Sciences","article_type":"original","quality_controlled":"1","publication_status":"published","date_created":"2022-07-18T14:37:39Z","article_processing_charge":"No","title":"FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants","intvolume":"       620","_id":"11618","scopus_import":"1","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":"García, R. A.","first_name":"R. A.","last_name":"García"},{"last_name":"Davies","first_name":"G. R.","full_name":"Davies, G. R."},{"full_name":"Mathur, S.","last_name":"Mathur","first_name":"S."},{"full_name":"Corsaro, E.","first_name":"E.","last_name":"Corsaro"},{"full_name":"Hall, O. J.","last_name":"Hall","first_name":"O. J."},{"full_name":"Rendle, B. M.","first_name":"B. M.","last_name":"Rendle"}],"volume":620,"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.","extern":"1","doi":"10.1051/0004-6361/201833106","arxiv":1,"day":"01","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."}],"date_updated":"2022-08-22T07:41:07Z","year":"2018","citation":{"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).","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>.","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.","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>","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>","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.","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>."},"external_id":{"arxiv":["1809.05105"]},"language":[{"iso":"eng"}],"keyword":["Space and Planetary Science","Astronomy and Astrophysics","asteroseismology / methods","data analysis / stars","oscillations"],"oa_version":"Preprint","month":"12","article_number":"A38","publication":"Astronomy & Astrophysics","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1809.05105"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","publication_identifier":{"eissn":["1432-0746"],"issn":["0004-6361"]},"oa":1,"date_published":"2018-12-01T00:00:00Z","type":"journal_article"},{"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."}],"day":"29","doi":"10.48550/arXiv.1811.12140","arxiv":1,"external_id":{"arxiv":["1811.12140"]},"type":"preprint","date_published":"2018-11-29T00:00:00Z","year":"2018","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.)."},"date_updated":"2022-08-22T08:41:55Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","extern":"1","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.1811.12140"}],"article_number":"1811.12140","title":"FliPer: Classifying TESS pulsating stars","month":"11","article_processing_charge":"No","date_created":"2022-07-21T07:05:23Z","publication_status":"submitted","oa_version":"Preprint","author":[{"id":"d9edb345-f866-11ec-9b37-d119b5234501","first_name":"Lisa Annabelle","last_name":"Bugnet","orcid":"0000-0003-0142-4000","full_name":"Bugnet, Lisa Annabelle"},{"first_name":"R. A.","last_name":"García","full_name":"García, R. A."},{"last_name":"Davies","first_name":"G. R.","full_name":"Davies, G. R."},{"first_name":"S.","last_name":"Mathur","full_name":"Mathur, S."},{"full_name":"Hall, O. J.","first_name":"O. J.","last_name":"Hall"},{"last_name":"Rendle","first_name":"B. M.","full_name":"Rendle, B. M."}],"publication":"arXiv","_id":"11631","keyword":["asteroseismology - methods","data analysis - stars","oscillations"],"language":[{"iso":"eng"}]},{"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","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","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>","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>. .","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>."},"year":"2017","date_published":"2017-11-08T00:00:00Z","type":"preprint","external_id":{"arxiv":["1711.02890"]},"language":[{"iso":"eng"}],"keyword":["asteroseismology - methods","data analysis - stars","oscillations"],"publication_status":"submitted","oa_version":"Preprint","date_created":"2022-07-21T07:13:13Z","article_processing_charge":"No","month":"11","title":"FliPer: Checking the reliability of global seismic parameters from automatic pipelines","article_number":"1711.02890","publication":"arXiv","_id":"11633","author":[{"id":"d9edb345-f866-11ec-9b37-d119b5234501","orcid":"0000-0003-0142-4000","full_name":"Bugnet, Lisa Annabelle","first_name":"Lisa Annabelle","last_name":"Bugnet"},{"last_name":"Garcia","first_name":"R. A.","full_name":"Garcia, R. A."},{"first_name":"G. R.","last_name":"Davies","full_name":"Davies, G. R."},{"full_name":"Mathur, S.","first_name":"S.","last_name":"Mathur"},{"first_name":"E.","last_name":"Corsaro","full_name":"Corsaro, E."}]}]