论文标题
鞍点近似的应用用于恒星光观测的周期检测
An application of Saddlepoint Approximation for period detection of stellar light observations
论文作者
论文摘要
分析恒星光曲线的主要特征之一是基本的周期性行为。相应的观察结果是一种复杂的时间序列类型，其时间点不均匀，有时还要伴随着不同的精度度量。 分析这些类型数据的主要工具取决于具有期间图的函数，该功能具有所需的特征，因此峰表明了潜在周期的存在。在本文中，我们探讨了类似于Thieler等人的不规则观察到的时间序列数据的特定期限图。 al。 （2013）。我们在适当的峰处确定潜在时期，更重要的是，具有可量化的不确定性。我们的方法被证明很容易推广到包括加权高斯过程回归期（包括加权高斯过程回归周期图）的非参数方法。我们还将这种方法扩展到相关的背景噪声。提出的周期检测方法依赖于基于具有正态分布组件的二次形式的测试。我们实现了鞍点近似，作为当前使用的基于仿真方法的更快，更准确的替代方法。使用狩猎爆发年轻明星公民科学项目中的光曲线进行了测试方法的功率分析以及应用。
One of the main features of interest in analysing the light curves of stars is the underlying periodic behaviour. The corresponding observations are a complex type of time series with unequally spaced time points and are sometimes accompanied by varying measures of accuracy. The main tools for analysing these type of data rely on the periodogram-like functions, constructed with a desired feature so that the peaks indicate the presence of a potential period. In this paper, we explore a particular periodogram for the irregularly observed time series data, similar to Thieler et. al. (2013). We identify the potential periods at the appropriate peaks and more importantly with a quantifiable uncertainty. Our approach is shown to easily generalise to non-parametric methods including a weighted Gaussian process regression periodogram. We also extend this approach to correlated background noise. The proposed method for period detection relies on a test based on quadratic forms with normally distributed components. We implement the saddlepoint approximation, as a faster and more accurate alternative to the simulation-based methods that are currently used. The power analysis of the testing methodology is reported together with applications using light curves from the Hunting Outbursting Young Stars citizen science project.
