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

TL;DR

This paper introduces an extension of the celerite Gaussian process method to two-dimensional data, enabling faster and more precise analysis of astrophysical time series such as exoplanet transits and transit spectroscopy.

Contribution

The paper presents a novel 2D celerite-based Gaussian process method that scales linearly with data size, improving analysis speed and accuracy for multiwavelength light curves.

Findings

Enhanced transit parameter measurement accuracy

Potential to improve exoplanet studies by orders of magnitude

Applicable to various astronomical time series analyses

Abstract

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 datapoints 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 datapoints 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.