Robust Period Estimation Using Mutual Information for Multiband Light Curves in the Synoptic Survey Era
Robust Period Estimation Using Mutual Information for Multiband Light Curves in the Synoptic Survey Era
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DOI：
10.3847/1538-4365/aab77c
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发表时间：
2017-09
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影响因子：
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通讯作者：
P. Huijse;P. Estévez;F. Förster;S. Daniel;A. Connolly;P. Protopapas;R. Carrasco;J. Príncipe
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文献类型：
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作者：
P. Huijse;P. Estévez;F. Förster;S. Daniel;A. Connolly;P. Protopapas;R. Carrasco;J. Príncipe
大型综合巡天望远镜（LSST）将使用六个光学波段产生前所未有的光变曲线。我们需要一种强大而有效的方法，可以从多维稀疏采样的时间序列中聚合数据。本文提出了一种基于二次互信息的光变周期估计方法。所提出的方法不假设光变曲线的特定模型，也不假设其潜在的概率密度，并且对非高斯噪声和离群值具有鲁棒性。通过组合来自多个频带的QMI，即使没有单频带QMI产生周期，也可以估计真实周期。周期恢复性能作为平均幅度和样本大小的函数，使用LSST操作和目录模拟器生成的RR Lyaeroid和Cepheid变量的30，000条合成多波段光变曲线进行测量。结果表明，从几个波段的信息聚合是非常有益的LSST稀疏采样时间序列，获得了绝对增加的周期恢复率高达50%。我们还表明，QMI是更强大的噪声和光曲线长度（样本量）比多波段概括的Lomb-Scargle和AoV周期图，恢复真正的周期在10%-30%的情况下比它的竞争对手。提供了一个python包，其中包含QMI和其他方法的高效Cython实现。
The Large Synoptic Survey Telescope (LSST) will produce an unprecedented amount of light curves using six optical bands. Robust and efficient methods that can aggregate data from multidimensional sparsely sampled time-series are needed. In this paper we present a new method for light curve period estimation based on quadratic mutual information (QMI). The proposed method does not assume a particular model for the light curve nor its underlying probability density and it is robust to non-Gaussian noise and outliers. By combining the QMI from several bands the true period can be estimated even when no single-band QMI yields the period. Period recovery performance as a function of average magnitude and sample size is measured using 30,000 synthetic multiband light curves of RR Lyrae and Cepheid variables generated by the LSST Operations and Catalog simulators. The results show that aggregating information from several bands is highly beneficial in LSST sparsely sampled time-series, obtaining an absolute increase in period recovery rate up to 50%. We also show that the QMI is more robust to noise and light curve length (sample size) than the multiband generalizations of the Lomb–Scargle and AoV periodograms, recovering the true period in 10%–30% more cases than its competitors. A python package containing efficient Cython implementations of the QMI and other methods is provided.