标题： 注意空隙：改进的检测非均匀采样数据周期性的方法 显示英文标题

标题： Mind the gaps: improved methods for the detection of periodicities in unevenly-sampled data

摘要： 在非规则采样的时间序列中检测周期信号是天文学中常见的问题。 传统的周期搜索工具（如周期图）在非规则采样下具有定义不明的统计性质，这使得推断用于假设检验的基础非周期变化变得复杂。 在存在随机变化的情况下，这个问题会更加严重，因为随机变化很容易被误认为是真实的周期行为，特别是在数据采样较差的情况下。 在这里，我们提出了一种基于高斯过程（GPs）建模的方法，用于周期搜索和特征描述，专门开发以克服这些问题。 我们认为，在非规则采样时间序列的情况下，高斯过程提供了比传统周期图更具吸引力的选择，因为已知的数据分布（相关高斯分布）允许构建一个明确的可能性。 我们利用这一特性，并借鉴现有的统计方法，使用从数据中推断出的非周期变化作为零假设，对额外的（准）周期成分进行传统的似然比检验。 从数据中推断噪声使该方法能够完全通用化，唯一的条件是数据可以被描述为高斯过程。 我们通过将其应用于显示不同噪声水平和数据质量的各种对象来演示该方法。 讨论了该方法的局限性，并公开提供实现所提出方法的软件包。 显示英文摘要

摘要： The detection of periodic signals in irregularly-sampled time series is a problem commonly encountered in astronomy. Traditional tools used for periodic searches, such as the periodogram, have poorly defined statistical properties under irregular sampling, which complicate inferring the underlying aperiodic variability used for hypothesis testing. The problem is exacerbated in the presence of stochastic variability, which can be easily mistaken by genuine periodic behaviour, particularly in the case of poorly sampled lightcurves. Here we present a method based on Gaussian Processes (GPs) modelling for period searches and characterization, specifically developed to overcome these problems. We argue that in cases of irregularly-sampled time series, GPs offer an appealing alternative to traditional periodograms, because the known distribution of the data (correlated Gaussian) allows a well-defined likelihood to be constructed. We exploit this property and draw from existing statistical methods to perform traditional likelihood ratio tests for an additional, (quasi-)periodic component, using the aperiodic variability inferred from the data as the null hypothesis. Inferring the noise from the data allows the method to be fully generalizable, with the only condition that the data can be described as a Gaussian process. We demonstrate the method by applying it to a variety of objects showing varying levels of noise and data quality. Limitations of the method are discussed and a package implementing the proposed methodology is made publicly available.