ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods
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 & Astrophysics. 647, A125.
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Author
Breton, S. N.;
Santos, A. R. G.;
Bugnet, LisaISTA ;
Mathur, S.;
García, R. A.;
Pallé, P. L.
Abstract
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.
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Publishing Year
Date Published
2021-03-19
Journal Title
Astronomy & Astrophysics
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
Article Number
A125
ISSN
eISSN
IST-REx-ID
Cite this
Breton SN, Santos ARG, Bugnet LA, Mathur S, García RA, Pallé PL. ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods. Astronomy & Astrophysics. 2021;647. doi:10.1051/0004-6361/202039947
Breton, S. N., Santos, A. R. G., Bugnet, L. A., Mathur, S., García, R. A., & Pallé, P. L. (2021). ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods. Astronomy & Astrophysics. EDP Sciences. https://doi.org/10.1051/0004-6361/202039947
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.” Astronomy & Astrophysics. EDP Sciences, 2021. https://doi.org/10.1051/0004-6361/202039947.
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,” Astronomy & Astrophysics, vol. 647. EDP Sciences, 2021.
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 & Astrophysics. 647, A125.
Breton, S. N., et al. “ROOSTER: A Machine-Learning Analysis Tool for Kepler Stellar Rotation Periods.” Astronomy & Astrophysics, vol. 647, A125, EDP Sciences, 2021, doi:10.1051/0004-6361/202039947.
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