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Gaussian processes (GPs) are commonly used as a model of stochasticvariability in astrophysical time series. In particular, GPs are frequentlyemployed to account for correlated stellar variability in planetary transitlight curves. The efficient application of GPs to light curves containingthousands to tens of thousands of datapoints has been made possible by recentadvances in GP methods, including the celerite method. Here we present anextension of the celerite method to two input dimensions, where, typically, thesecond dimension is small. This method scales linearly with the total number ofdatapoints when the noise in each large dimension is proportional to the samecelerite kernel and only the amplitude of the correlated noise varies in thesecond dimension. We demonstrate the application of this method to the problemof measuring precise transit parameters from multiwavelength light curves andshow that it has the potential to improve transit parameters measurements byorders of magnitude. Applications of this method include transit spectroscopyand exomoon detection, as well a broader set of astronomical problems.

A Fast, 2D Gaussian Process Method Based on Celerite: Applications to Transiting Exoplanet Discovery and Characterization