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中国物理学会期刊

基于近似熵的斯隆数字化巡天中类星体光变复杂性分析

CSTR: 32037.14.aps.68.20182071

Analysis on complexity of optical variability based on approximate entropy in Sloan digital sky survey quasars

CSTR: 32037.14.aps.68.20182071
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  • 光变是类星体的重要观测特征之一, 类星体在多个波段存在剧烈的光变现象. 光变非常复杂, 具有非线性特征. 以斯隆数字化巡天(Sloan digital sky survey, SDSS) stripe 82天区中的类星体为研究对象, 利用近似熵方法分析了类星体光变的复杂性. 首先应用模拟信号检验了近似熵方法对周期序列、白噪声序列、混沌序列和组合序列的区分能力, 验证了近似熵方法是一种识别不同类型时间序列的有效方法. 再计算了SDSS第7次释放数据中光谱证认过的类星体光变的近似熵, 并分析了它们的复杂性. 结果表明: SDSS类星体光变的近似熵值最大值为0.58, 类星体光变的复杂性介于周期序列和白噪声序列的复杂性之间, 近一半样本的复杂性与混沌序列基本一致.

     

    Variability is one of the most important observational features of quasars, and it is still not clear that the different quasars show different characteristic variability patterns. The optical variability of quasar is very complex, and optical variability has the non-linear characteristic of complex system. In this paper, the approximate entropy method is employed to analyze the complexities of variability in the Sloan digital sky survey (SDSS) stripe 82 quasars. Firstly, in order to show that the approximate entropy method has the effective ability to distinguish the different types of time sequences, the approximate entropy of periodic sequence, noise sequence, chaotic sequence and their mixed sequences are calculated by using the analog signals. The approximate entropy method proves to be an effective method to identify different types of time sequences. Then, we present the approximate entropy of optical variability of spectroscopically-confirmed quasars from the SDSS data release 7 spectroscopic catalog, and their complexities are analyzed. The results show that the maximum approximate entropy of quasars’ optical variability is only 0.58. The complexity of quasars’ optical variability is between the complexities of periodic sequence and white noise sequence. For nearly half of the samples, the complexities of their optical variability are basically consistent with the complexity of chaotic sequence. Quasars’ optical variability is neither completely periodic nor completely stochastic.

     

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