Abstract
Aims. We study the distribution of the photometric rotation period
(P-rot), which is a direct measurement of the surface rotation at active
latitudes, for three subsamples of Sun-like stars: one from CoRoT data
and two from Kepler data. For this purpose, we identify the main
populations of these samples and interpret their main biases
specifically for a comparison with the solar P-rot.
Methods. P-rot and variability amplitude (A) measurements were obtained
from public CoRoT and Kepler catalogs, which were combined with public
data of physical parameters. Because these samples are subject to
selection effects, we computed synthetic samples with simulated biases
to compare with observations, particularly around the location of the
Sun in the Hertzsprung-Russel (HR) diagram. Publicly available
theoretical grids and empirical relations were used to combine physical
parameters with P-rot and A. Biases were simulated by performing cutoffs
on the physical and rotational parameters in the same way as in each
observed sample. A crucial cutoff is related with the detectability of
the rotational modulation, which strongly depends on A.
Results. The synthetic samples explain the observed P-rot distributions
of Sun-like stars as having two main populations: one of young objects
(group I, with ages younger than similar to 1 Gyr) and another of
main-sequence and evolved stars (group II, with ages older than similar
to 1 Gyr). The proportions of groups I and II in relation to the total
number of stars range within 64-84% and 16-36%, respectively. Hence,
young objects abound in the distributions, producing the effect of
observing a high number of short periods around the location of the Sun
in the HR diagram. Differences in the P-rot distributions between the
CoRoT and Kepler Sun-like samples may be associated with different
Galactic populations. Overall, the synthetic distribution around the
solar period agrees with observations, which suggests that the solar
rotation is normal with respect to Sun-like stars within the accuracy of
current data.
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