arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.14040astro-ph.HE
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Gamma-ray Blazar变率:时间-通量分布的新统计方法

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

Jaroslaw Duda, Gopal Bhatta

摘要:

耀变体的\gama -射线辐射是一个广泛讨论的话题,耀变体是最强大的天文源之一,具有相对论喷流。本文利用Fermi/LAT望远镜的\gama射线(0.1- 300 ~GeV)观测资料,对20颗耀变体进行了光变研究。使用极大似然估计(MLE)方法,我们发现,最好的描述$γ$射线blazar流量分布的概率密度函数使用稳定的分布族,它推广了高斯分布。结果表明,在此期间的平均行为的\gama -射线通量的变化,可以用对数稳定分布。对于大多数样本源,该估计导致标准对数正态分布($α=2$)。然而,少数来源清楚地显示重尾分布(MLE导致$α<2$),这表明潜在的乘法过程的无限方差。此外,光变曲线进行了分析,采用新的非平稳性和自相关分析。前一个分析使我们能够定量评估每个源的非平稳性-找到遗忘率(对应于衰减时间)最大化概率密度函数模型演化的对数似然。此外,局部变异性的评估使我们能够检测到局部异常,这表明光变曲线的一些统计特性具有瞬态性质。与自相关分析,我们研究了滞后依赖的所有$\{(y_t,y_{t+l})\}$点的统计行为,描述了各种混合的时刻,使我们能够定量评估多个特征时间尺度,并暗示可能隐藏的周期性过程。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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