论文标题

Gamma-ray Blazar的可变性:新的统计方法分布的统计方法

Gamma-ray Blazar variability: New statistical methods of time-flux distributions

论文作者

Duda, Jaroslaw, Bhatta, Gopal

论文摘要

Blazars的可变\ GAMA射线排放是以相对论喷气机为特色的最强大的天文来源之一,是一个广泛讨论的主题。在这项工作中,我们使用\ Gama-ray(0.1---300〜GEV)观察到Fermi/LAT望远镜的观察结果介绍了20种Blazars样本的可变性研究结果。使用最大似然估计(MLE)方法,我们发现最能描述$γ$ -Ray-ray blazar通量分布的概率密度函数使用稳定的分布家族,该家族概述了高斯分布。结果表明,在此期间,\ GAMA射线通量变异性的平均行为可以以对数稳定分布为特征。对于大多数样本源,此估计值导致标准的对数正态分布($α= 2 $)。但是,一些来源清楚地显示了较重的尾巴分布(MLE导致$α<2 $),这表明无限差异的乘法过程的基础。此外,通过采用新颖的非平稳性和自相关分析来分析光曲线。前者的分析使我们能够定量评估每个源中的非平稳性 - 找到遗忘率(对应于衰减时间)最大化概率密度函数的模型演化的对数可能性。此外,对局部变异性的评估使我们能够检测局部异常,这表明光曲线的某些统计特性具有短暂性。通过自相关分析,我们检查了所有$ \ {(y_t,y_ {t+l})$点的统计行为的滞后依赖性,由各种混合力矩描述,允许我们定量评估多个特征时间尺度并暗示可能的隐藏周期性过程。

Variable \gama-ray emission from blazars, one of the most powerful classes of astronomical sources featuring relativistic jets, is a widely discussed topic. In this work, we present the results of a variability study of a sample of 20 blazars using \gama-ray (0.1--300~GeV) observations from Fermi/LAT telescope. Using maximum likelihood estimation (MLE) methods, we find that the probability density functions that best describe the $γ$-ray blazar flux distributions use the stable distribution family, which generalizes the Gaussian distribution. The results suggest that the average behavior of the \gama-ray flux variability over this period can be characterized by log-stable distributions. For most of the sample sources, this estimate leads to standard log-normal distribution ($α=2$). However, a few sources clearly display heavy tail distributions (MLE leads to $α<2$), suggesting underlying multiplicative processes of infinite variance. Furthermore, the light curves were analyzed by employing novel non-stationarity and autocorrelation analyses. The former analysis allowed us to quantitatively evaluate non-stationarity in each source -- finding the forgetting rate (corresponding to decay time) maximizing the log-likelihood for the modeled evolution of the probability density functions. Additionally, evaluation of local variability allows us to detect local anomalies, suggesting a transient nature of some of the statistical properties of the light curves. With the autocorrelation analysis, we examined the lag dependence of the statistical behavior of all the $\{(y_t,y_{t+l})\}$ points, described by various mixed moments, allowing us to quantitatively evaluate multiple characteristic time scales and implying possible hidden periodic processes.

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