相关噪声的有效建模。III。多观测量时间序列高斯过程联合建模的可扩展方法
Efficient modeling of correlated noise. III. Scalable methods for jointly modeling several observables' time series with Gaussian processes
摘要：
径向速度法是一种非常有效的用于探测和确认太阳系外行星的技术。最新的摄谱仪，如ESTERO或EXPRESS，有可能探测到类太阳恒星周围的类地行星。然而，恒星活动会引起径向速度的变化，从而冲淡甚至模仿行星的特征。一个被广泛认可的方法来解开这些信号是模拟径向速度时间序列，连同恒星活动指标，使用高斯过程及其衍生物。然而，这样的建模在用于大数据集的计算资源方面是禁止的，因为成本通常随着测量的总数的立方而缩放。在这里，我们提出了S+LEAF 2，这是一个高斯过程框架，可用于联合建模多个时间序列，其计算成本与数据集大小呈线性关系。因此，该框架提供了一个最先进的高斯过程模型，即使对于大型数据集也具有易于处理的计算。我们通过重新分析附近K2矮星HD 138038的246个HARPS径向速度测量值以及两个活动指标来说明这个框架的力量。我们重现了这些数据的先前分析的结果，但计算成本大大降低（超过两个数量级）。对于更大的数据集，增益甚至更大。
英文摘要：
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.
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