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NONPARAMETRIC ESTIMATION OF GAMMA-RAY BURST INTENSITIES USING HAAR WAVELETS

机译:基于Haar小波的伽玛射线爆裂强度的非参数估计。

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摘要

In this article, I present a method for the nonparametric (model-free) estimation of intensity profiles underlying gamma-ray bursts. The algorithm, called TIPSH, is based on applying specially calibrated thresholds to the Haar wavelet coefficients of binned counts gathered from such bursts. Because wavelets are well-localized with respect to both time and scale, they are an ideal tool for working with the often sharp, abrupt nature of gamma-ray burst signals. When applied to an idealized signal in a small simulation study and a selection of actual gamma-ray bursts, the TIPSH algorithm was found to be well capable of simultaneously estimating the smooth, uniform background and the pulse-like structure of gamma-ray burst signals.
机译:在本文中,我提出了一种用于对伽马射线爆发基础强度分布进行非参数(无模型)估计的方法。称为TIPSH的算法基于对经过此类突发收集的合并计数的Haar小波系数应用经过特殊校准的阈值。由于小波在时间和尺度方面都很好地定位,因此它们是处理通常具有尖锐,突然性质的伽马射线猝发信号的理想工具。当在小型仿真研究中将理想化信号应用于理想的信号并选择实际的伽马射线突发时,发现TIPSH算法能够很好地同时估计伽马射线突发信号的平滑,均匀背景和脉冲状结构。

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