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Determination of rotation periods in solar-like stars with irregular sampling: the Gaia case

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 Added by Elisa Distefano
 Publication date 2012
  fields Physics
and research's language is English
 Authors E. Distefano




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We present a study on the determination of rotation periods (P) of solar-like stars from the photometric irregular time-sampling of the ESA Gaia mission, currently scheduled for launch in 2013, taking into account its dependence on ecliptic coordinates. We examine the case of solar-twins as well as thousands of synthetic time-series of solar-like stars rotating faster than the Sun. In the case of solar twins we assume that the Gaia unfiltered photometric passband G will mimic the variability of the total solar irradiance (TSI) as measured by the VIRGO experiment. For stars rotating faster than the Sun, light-curves are simulated using synthetic spectra for the quiet atmosphere, the spots, and the faculae combined by applying semi-empirical relationships relating the level of photospheric magnetic activity to the stellar rotation and the Gaia instrumental response. The capabilities of the Deeming, Lomb-Scargle, and Phase Dispersion Minimisation methods in recovering the correct rotation periods are tested and compared. The false alarm probability (FAP) is computed using Monte Carlo simulations and compared with analytical formulae. The Gaia scanning law makes the rate of correct detection of rotation periods strongly dependent on the ecliptic latitude (beta). We find that for P ~ 1 d, the rate of correct detection increases with ecliptic latitude from 20-30 per cent at beta ~ 0{deg} to a peak of 70 per cent at beta=45{deg}, then it abruptly falls below 10 per cent at beta > 45{deg}. For P > 5 d, the rate of correct detection is quite low and for solar twins is only 5 per cent on average.



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We use various method to extract surface rotation periods of Kepler targets exhibiting solar-like oscillations and compare their results.
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For a solar-like star, the surface rotation evolves with time, allowing in principle to estimate the age of a star from its surface rotation period. Here we are interested in measuring surface rotation periods of solar-like stars observed by the NASA Kepler mission. Different methods have been developed to track rotation signals in Kepler photometric light curves: time-frequency analysis based on wavelet techniques, autocorrelation and composite spectrum. We use the learning abilities of random forest classifiers to take decisions during two crucial steps of the analysis. First, given some input parameters, we discriminate the considered Kepler targets between rotating MS stars, non-rotating MS stars, red giants, binaries and pulsators. We then use a second classifier only on the MS rotating targets to decide the best data-analysis treatment.
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