Surface rotation and photometric activity for Kepler targets. II. G and F main-sequence stars, and cool subgiant stars

Abstract

Dark magnetic spots crossing the stellar disc lead to quasi-periodic brightness variations, which allow us to constrain stellar surface rotation and photometric activity. The current work is the second of this series (Santos et al. 2019, Paper I), where we analyze the Kepler long-cadence data of 132,921 main-sequence F and G stars and late subgiant stars. Rotation-period candidates are obtained by combining wavelet analysis with autocorrelation function. Reliable rotation periods are then selected via a machine learning (ML) algorithm (Breton et al. 2021), automatic selection, and complementary visual inspection. The ML training data set comprises 26,521 main-sequence K and M stars from Paper I. To supplement the training, we analyze in the same way as Paper I, i.e. automatic selection and visual inspection, 34,100 additional stars. We finally provide rotation periods P rot and associated photometric activity proxy S ph for 39,592 targets. Hotter stars are generally faster rotators than cooler stars. For main-sequence G stars, S ph spans a wider range of values with increasing effective temperature, while F stars tend to have smaller S ph values in comparison with cooler stars. Overall for G stars, fast rotators are photometrically more active than slow rotators, with S ph saturating at short periods. The combined outcome of the two papers accounts for average P rot and S ph values for 55,232 main-sequence and subgiant FGKM stars (out of 159,442 targets), with 24,182 new P rot detections in comparison with McQuillan et al. (2014). The upper edge of the P rot distribution is located at longer P rot than found previously.

On the relation between active-region lifetimes and the autocorrelation function of light curves

“…The autocorrelation function of stellar light curves has been extensively used in the literature to constrain surface rotation periods (e.g. McQuillan et al 2013McQuillan et al , 2014García et al 2014;Santos et al 2019Santos et al , 2021. In addition, as the ACF reflects the coherence of the signal, the decay timescale of the ACF is expected to be related to the spot or active-region lifetimes (e.g.…”

“…While spot evolution may be hard to tackle, beating patterns can be identified through photometric magnetic activity metrics like 𝑆 ph (Mathur et al 2014), as beating patterns affect such metrics. One group of targets, in particular, flagged in Santos et al (2019Santos et al ( , 2021 as close-in binary candidates, exhibits stable and, often, fast beating patterns. These targets have similar behaviour to targets identified as tidally-synchronized binaries by Simonian et al (2019, see further discussion in Santos et al 2019.…”

“…We adopt a Gaussian distribution with mean value corresponding to 𝑃 eq and a standard deviation being 20% of 𝑃 eq . For example, on average the uncertainty on the rotation period from the wavelet analysis for Kepler targets is 10% (Santos et al 2019(Santos et al , 2021. A standard deviation of 20% is also sufficient to account for the differential rotation adopted in this work.…”

“…For those light curves exhibiting spot modulation, it has been possible to constrain activity-related properties, such as average surface rotation (e.g. McQuillan et al 2014;Mathur et al 2014;García et al 2014;Santos et al 2019Santos et al , 2021Godoy-Rivera et al 2021), differential rotation (e.g. Reinhold et al 2013;Karoff et al 2018), and average photometric magnetic activity (e.g.…”

“…Reinhold et al 2013;Karoff et al 2018), and average photometric magnetic activity (e.g. Mathur et al 2014;García et al 2014;Santos et al 2019Santos et al , 2021. Another property that, in principle, can be constrained from the spot modulation is the lifetimes of active regions.…”