使用循环神经网络验证周期性
The verification of periodicity with the use of recurrent neural networks
AI总结：
针对周期图法受光变曲线形状等非周期因素影响及高斯误差假设局限，本文提出用循环神经网络直接分析相位折叠光变曲线以估计误报概率，并确定数据点数和振幅噪声比阈值。
摘要：
随着数据集规模迅速增大，自动且稳健地自验证天文时间序列数据中存在的周期性的能力正变得愈发重要。大型天文巡天时代已使人工检查时间序列数据变得不那么现实。此前通过生成误报概率来验证恒星周期性的工作，主要针对所构建的周期图进行分析。然而，这些方法会与一些和周期性无关的特征产生相关性，例如光变曲线形状、缓慢趋势和随机变率。测光误差服从高斯分布且已被良好确定这一常见假设，同样是解析方法的一项局限。我们提出一种基于机器学习的新技术，直接分析相位折叠光变曲线以计算其误报概率。我们表明，该方法的结果在很大程度上不受光变曲线形状影响，并确定了数据点数量以及振幅噪声比的最小值。
英文摘要：
The ability to automatically and robustly self-verify periodicity present in time-series astronomical data is becoming more important as data sets rapidly increase in size. The age of large astronomical surveys has rendered manual inspection of time-series data less practical. Previous efforts in generating a false alarm probability to verify the periodicity of stars have been aimed towards the analysis of a constructed periodogram. However, these methods feature correlations with features that do not pertain to periodicity, such as light curve shape, slow trends and stochastic variability. The common assumption that photometric errors are Gaussian and well determined is also a limitation of analytic methods. We present a novel machine learning based technique which directly analyses the phase folded light curve for its false alarm probability. We show that the results of this method are largely insensitive to the shape of the light curve, and we establish minimum values for the number of data points and the amplitude to noise ratio.
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