@article{11608,
  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.},
  author       = {Breton, S. N. and Santos, A. R. G. and Bugnet, Lisa Annabelle and Mathur, S. and García, R. A. and Pallé, P. L.},
  issn         = {1432-0746},
  journal      = {Astronomy & Astrophysics},
  keywords     = {Space and Planetary Science, Astronomy and Astrophysics, methods: data analysis / stars: solar-type / stars: activity / stars: rotation / starspots},
  publisher    = {EDP Sciences},
  title        = {{ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods}},
  doi          = {10.1051/0004-6361/202039947},
  volume       = {647},
  year         = {2021},
}

@article{11623,
  abstract     = {Brightness variations due to dark spots on the stellar surface encode information about stellar surface rotation and magnetic activity. In this work, we analyze the Kepler long-cadence data of 26,521 main-sequence stars of spectral types M and K in order to measure their surface rotation and photometric activity level. Rotation-period estimates are obtained by the combination of a wavelet analysis and autocorrelation function of the light curves. Reliable rotation estimates are determined by comparing the results from the different rotation diagnostics and four data sets. We also measure the photometric activity proxy Sph using the amplitude of the flux variations on an appropriate timescale. We report rotation periods and photometric activity proxies for about 60% of the sample, including 4431 targets for which McQuillan et al. did not report a rotation period. For the common targets with rotation estimates in this study and in McQuillan et al., our rotation periods agree within 99%. In this work, we also identify potential polluters, such as misclassified red giants and classical pulsator candidates. Within the parameter range we study, there is a mild tendency for hotter stars to have shorter rotation periods. The photometric activity proxy spans a wider range of values with increasing effective temperature. The rotation period and photometric activity proxy are also related, with Sph being larger for fast rotators. Similar to McQuillan et al., we find a bimodal distribution of rotation periods.},
  author       = {Santos, A. R. G. and García, R. A. and Mathur, S. and Bugnet, Lisa Annabelle and van Saders, J. L. and Metcalfe, T. S. and Simonian, G. V. A. and Pinsonneault, M. H.},
  issn         = {0067-0049},
  journal      = {The Astrophysical Journal Supplement Series},
  keywords     = {Space and Planetary Science, Astronomy and Astrophysics, methods: data analysis, stars: activity, stars: low-mass, stars: rotation, starspots, techniques: photometric},
  number       = {1},
  publisher    = {IOP Publishing},
  title        = {{Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars}},
  doi          = {10.3847/1538-4365/ab3b56},
  volume       = {244},
  year         = {2019},
}