No abstract

We analyze light curves of 284,834 unique K2 targets using a Gaussian process model with a quasi-periodic kernel function. By cross-matching K2 stars to observations from Gaia Data Release 2, we have identified 69,627 likely main-sequence stars. From these we select a subsample of 8977 stars on the main sequence with highly precise rotation period measurements. With this sample we recover the gap in the rotation period−color diagram first reported by McQuillan et al. While the gap was tentatively detected in Reinhold & Hekker, this work represents the first robust detection of the gap in K2 data for field stars. This is significant because K2 observed along many lines of sight at wide angular separation, in contrast to Kepler’s single line of sight. Together with recent results for rotation in open clusters, we interpret this gap as evidence for a departure from the t −1/2 Skumanich spin-down law, rather than an indication of a bimodal star formation history. We provide maximum likelihood estimates and uncertainties for all parameters of the quasi-periodic light-curve model for each of the 284,834 stars in our sample.

Gaussian processes (GPs) are commonly used as a model of stochastic variability in astrophysical time series. In particular, GPs are frequently employed to account for correlated stellar variability in planetary transit light curves. The efficient application of GPs to light curves containing thousands to tens of thousands of data points has been made possible by recent advances in GP methods, including the celerite method. Here we present an extension of the celerite method to two input dimensions where, typically, the second dimension is small. This method scales linearly with the total number of data points when the noise in each large dimension is proportional to the same celerite kernel and only the amplitude of the correlated noise varies in the second dimension. We demonstrate the application of this method to the problem of measuring precise transit parameters from multiwavelength light curves and show that it has the potential to improve transit parameters measurements by orders of magnitude. Applications of this method include transit spectroscopy and exomoon detection, as well a broader set of astronomical problems.

We present a solution for the light curve of two bodies mutually transiting a star with polynomial limb darkening. The term “mutual transit” in this work refers to a transit of the star during which overlap occurs between the two transiting bodies. These could be an exoplanet with an exomoon companion, two exoplanets, an eclipsing binary and a planet, or two stars eclipsing a third in a triple-star system. We include analytic derivatives of the light curve with respect to the positions and radii of both bodies. We provide code that implements a photodynamical model for a mutual transit. We include two dynamical models, one for hierarchical systems in which a secondary body orbits a larger primary (e.g., an exomoon system) and a second for confocal systems in which two bodies independently orbit a central mass (e.g., two planets in widely separated orbits). Our code is fast enough to enable inference with Markov Chain Monte Carlo algorithms, and the inclusion of derivatives allows for the use of gradient-based inference methods such as Hamiltonian Monte Carlo. While applicable to a variety of systems, this work was undertaken primarily with exomoons in mind. It is our hope that making this code publicly available will reduce barriers for the community to assess the detectability of exomoons, conduct searches for exomoons, and attempt to validate existing exomoon candidates. We also anticipate that our code will be useful for studies of planet–planet transits in exoplanetary systems, transits of circumbinary planets, and eclipses in triple-star systems.