论文标题
基于Celerite的快速2D高斯过程方法：用于传播系外行星的应用和表征
A Fast, 2D Gaussian Process Method Based on Celerite: Applications to Transiting Exoplanet Discovery and Characterization
论文作者
论文摘要
高斯过程（GPS）通常用作天体物理时间序列中随机变异性的模型。特别是，GP经常被用来解释行星过境光曲线中相关的恒星变异性。 GP在包括Celerite方法在内的GP方法的最新进展使GP的有效应用到包含数千到数千个数据点的光曲线已成为可能。在这里，我们将celerite方法的扩展为两个输入尺寸，其中二维通常很小。当每个大维中的噪声与相同的celerite内核成比例时，此方法与数据点的总数线性缩放，并且仅相关噪声的幅度在第二个维度上有所不同。我们证明了该方法在从多波长光曲线中测量精确过境参数的问题中的应用，并表明它具有通过数量级来改善运输参数测量值的潜力。该方法的应用包括过境光谱和外事物检测，以及一组更广泛的天文问题。
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.
