论文标题
检测具有高斯过程的高度不规则采样曲折的周期性：SDSSJ025214.67-002813.7的情况
Detecting the periodicity of highly irregularly sampled light-curves with Gaussian processes: the case of SDSSJ025214.67-002813.7
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Based on a 20-year-long multiband observation of its light-curve, it was conjectured that the quasar SDSSJ025214.67-002813.7 has a periodicity of ~4.4 years. These observations were acquired at a highly irregular sampling rate and feature long intervals of missing data. In this setting, the inference over the light-curve's spectral content requires, in addition to classic Fourier methods, a proper model of the probability distribution of the missing observations. In this article, we address the detection of the periodicity of a light-curve from partial and irregularly-sampled observations using Gaussian processes, a Bayesian nonparametric model for time series. This methodology allows us to evaluate the veracity of the claimed periodicity of the abovementioned quasar and also to estimate its power spectral density. Our main contribution is the confirmation that considering periodic component definitely improves the modeling of the data, although being the source originally selected by a large sample of objects, the possibility that this is a chance result cannot be ruled out.
