@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{11618,
  abstract     = {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.},
  author       = {Bugnet, Lisa Annabelle and García, R. A. and Davies, G. R. and Mathur, S. and Corsaro, E. and Hall, O. J. and Rendle, B. M.},
  issn         = {1432-0746},
  journal      = {Astronomy & Astrophysics},
  keywords     = {Space and Planetary Science, Astronomy and Astrophysics, asteroseismology / methods, data analysis / stars, oscillations},
  publisher    = {EDP Sciences},
  title        = {{FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants}},
  doi          = {10.1051/0004-6361/201833106},
  volume       = {620},
  year         = {2018},
}

@unpublished{11631,
  abstract     = {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
the 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.},
  author       = {Bugnet, Lisa Annabelle and García, R. A. and Davies, G. R. and Mathur, S. and Hall, O. J. and Rendle, B. M.},
  booktitle    = {arXiv},
  keywords     = {asteroseismology - methods, data analysis - stars, oscillations},
  title        = {{FliPer: Classifying TESS pulsating stars}},
  doi          = {10.48550/arXiv.1811.12140},
  year         = {2018},
}

@unpublished{11633,
  abstract     = {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.},
  author       = {Bugnet, Lisa Annabelle and Garcia, R. A. and Davies, G. R. and Mathur, S. and Corsaro, E.},
  booktitle    = {arXiv},
  keywords     = {asteroseismology - methods, data analysis - stars, oscillations},
  title        = {{FliPer: Checking the reliability of global seismic parameters from automatic pipelines}},
  doi          = {10.48550/arXiv.1711.02890},
  year         = {2017},
}