论文标题
相关噪声的有效建模I.周期图峰的统计显着性
Efficient modeling of correlated noise I. Statistical significance of periodogram peaks
论文作者
论文摘要
周期图是用于搜索不均间隔时间序列的定期信号的常见工具。经常使用虚假警报概率（FAP）评估周期峰的重要性，在大多数研究中，该峰值假设噪声并使用数值方法（例如自举或蒙特卡洛）进行计算。这些方法具有很高的计算成本，尤其是对于最感兴趣的低FAP水平。我们介绍了在存在相关噪声的情况下对周期图FAP的分析估计，这对于正确分析天文时间序列是基本的。我们得出的分析估计值以比数值方法低得多的成本提供了很好的FAP近似值。我们通过将分析方法与蒙特卡洛模拟进行比较来验证我们的分析方法。最后，我们讨论了该方法对噪声建模中不同假设的敏感性。
Periodograms are common tools used to search for periodic signals in unevenly spaced time series. The significance of periodogram peaks is often assessed using false alarm probability (FAP), which in most studies assumes uncorrelated noise and is computed using numerical methods such as bootstrapping or Monte Carlo. These methods have a high computational cost, especially for low FAP levels, which are of most interest. We present an analytical estimate of the FAP of the periodogram in the presence of correlated noise, which is fundamental to analyze astronomical time series correctly. The analytical estimate that we derive provides a very good approximation of the FAP at a much lower cost than numerical methods. We validate our analytical approach by comparing it with Monte Carlo simulations. Finally, we discuss the sensitivity of the method to different assumptions in the modeling of the noise.
