Statistics of Stellar Variability in Kepler Data with ARC Systematics Removal

Abstract

Abstract. We investigate the variability properties of main-sequence stars in the first month of Kepler data, using a new astrophysically robust systematics correction. We find that 36% appear more variable than the Sun, and confirm the trend of increasing variability with decreasing effective temperature. We define low-and high-variability samples, with a cut at twice the level of the active Sun, and compare properties of the stars belonging to each sample. We find tentative evidence that the more active stars have lower proper motions. The frequency content of the variability shows clear evidence for periodic or quasi-periodic behaviour in 16% of stars, and highlights significant differences in the nature of variability between spectral types. Most A and F stars have short periods (< 2 days) and highly sinusoidal variability, suggestive of pulsations, whilst G, K and M stars tend to have longer periods (> 5 days, with a trend towards longer periods at later spectral types) and show a mixture of periodic and stochastic variability, indicative of activity. Finally, we use autoregressive models to characterise the stochastic component of the variability, and show that its typical amplitude and time-scale increase towards later spectral types, which we interpret as an increase in the characteristic size and life-time of active regions. Full details will be published shortly.

Inferring probabilistic stellar rotation periods using Gaussian processes

“…We use an informed period prior, based on the autocorrelation function (ACF) of the light curve. The ACF has proven to be very useful for measuring stellar rotation periods (McQuillan et al 2012(McQuillan et al , 2013b(McQuillan et al , 2014; however, the method has several shortcomings, most notably the inability to deliver uncertainties, but also the necessity of several heuristic choices, such as a timescale on which to smooth the ACF, how to define a peak, whether the first or second peak gets selected, and what constitutes a secure detection. While this paper presents a rotation period inference method that avoids these shortcomings, it seems prudent to still use information available from the ACF.…”