论文标题
相关噪声的有效建模。 iii。与高斯过程共同建模几个可观察到的时间序列的可扩展方法
Efficient modeling of correlated noise. III. Scalable methods for jointly modeling several observables' time series with Gaussian processes
论文作者
论文摘要
径向速度方法是一种非常有生产力的技术，用于检测和确认极性行星。最新的光谱仪，例如浓缩咖啡或表格，有可能在阳光般的恒星周围检测类似地球的行星。但是，恒星活性可以诱导径向速度变化，从而稀释甚至模仿行星的特征。通过高斯工艺及其衍生物共同认可了脱离这些信号的一种广泛认可的方法，它是与恒星活性指标共同建模的。但是，对于大型数据集的计算资源，这种建模通常是成本量表，因为成本通常是测量的总数。 在这里，我们提出了S+LEAF 2，这是一个高斯过程框架，可用于联合建模几个时间序列，其计算成本与数据集大小线性缩放。因此，该框架提供了一个最新的高斯流程模型，即使对于大型数据集，也可以使用可进行的计算。 我们通过重新分析附近K2矮人HD 138038的246个竖琴径向速度测量以及两个活动指标来说明该框架的功能。我们重现了对这些数据的先前分析的结果，但计算成本大幅下降（超过两个数量级）。对于较大的数据集，增益将更大。
The radial velocity method is a very productive technique used to detect and confirm extrasolar planets. The most recent spectrographs, such as ESPRESSO or EXPRES, have the potential to detect Earth-like planets around Sun-like stars. However, stellar activity can induce radial velocity variations that dilute or even mimic the signature of a planet. A widely recognized method for disentangling these signals is to model the radial velocity time series, jointly with stellar activity indicators, using Gaussian processes and their derivatives. However, such modeling is prohibitive in terms of computational resources for large data sets, as the cost typically scales as the total number of measurements cubed. Here, we present S+LEAF 2, a Gaussian process framework that can be used to jointly model several time series, with a computational cost that scales linearly with the data set size. This framework thus provides a state-of-the-art Gaussian process model, with tractable computations even for large data sets. We illustrate the power of this framework by reanalyzing the 246 HARPS radial velocity measurements of the nearby K2 dwarf HD 138038, together with two activity indicators. We reproduce the results of a previous analysis of these data, but with a strongly decreased computational cost (more than two order of magnitude). The gain would be even greater for larger data sets.
