伽玛暴微光变特征时标研究

发布时间：2018-11-08 11:00

【摘要】：伽玛暴光变曲线一般由若干个脉冲组成的"大轮廓"叠加着一些微光变组成的,大部分伽玛暴光变曲线没有呈现周期性,而这些微光变成分可能携带重要的辐射区性质。采用平滑滤波器把伽玛暴光变曲线中的"大轮廓"成分和微光变成分分离,然后采用"最大熵"功率谱密度估算法计算微光变信号的功率谱密度,并分析其周期特征时标。文中分析了Swift/BAT所探测的爆发持续时间大于20 s、采样间隔为1s的伽玛暴,发现有17个伽玛暴的微光变信号的功率谱密度函数曲线呈现显著的"峰"结构,表明这些微光变信号具有周期特征光变时标,这些时标一般约为4～8 s。如果"大轮廓"的脉冲信号和微光变信号是两个本质不同的辐射成分,根据火球模拟可计算微光变信号的辐射区尺度应该在1.5×1013～1.5×1015cm,比内激波辐射区尺度略大,在这一区域内有可能微光变信号受到能量较大的伽玛射线的调制而叠加到"大轮廓"光变信号中,从而形成复杂多样的伽玛暴光变曲线。
[Abstract]:The gamma-ray curve is generally composed of a number of pulses "big contour" superposed by some low-light variations, most of the gamma-ray curve does not show periodicity, and these low-light levels may carry important radiation zone properties. A smoothing filter is used to separate the "large contour" component from the low-light level in the gamma-ray variation curve, and then the "maximum entropy" power spectral density estimation method is used to calculate the power spectral density of the low-light varying signal, and its periodic characteristic time scale is analyzed. In this paper, the burst duration of more than 20 s and sampling interval of 1 s detected by Swift/BAT are analyzed. It is found that the power spectral density function curve of the LLL variable signal of 17 gamma storms shows a significant "peak" structure. It is shown that these LLL signals have periodic characteristic time scales, which are generally about 4 ~ 8 s. If the pulse signal and the low-light variable signal of the "big outline" are two essentially different radiation components, the size of the radiation region of the low-light-varying signal which can be calculated according to the fireball simulation should be 1.5 脳 1013 ~ 1.5 脳 10 ~ (15) cm, which is slightly larger than that of the internal shock wave radiation region. In this region, it is possible that the low-light change signal is modulated by the high-energy gamma ray and superimposed into the "big contour" light variant signal, thus forming complex and varied gamma-ray variation curves.
【作者单位】：广西大学物理科学与工程技术学院;
【基金】：国家自然科学基金资助项目(10847003) 广西自然科学基金资助项目(2010GXNSFC013011)
【分类号】：P172.3

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