论文标题
一种用高斯固定噪声在时间序列中增强瞬态源检测灵敏度的方法
A method of enhancing the detection sensitivity of transient sources in time series with Gaussian stationary noise
论文作者
论文摘要
强度时间序列的高斯相位噪声被证明是当原始电压数据通过任意较大的$ n $ n $正态带通道图谱（特征滤波器）分享相同强度带宽的数字$ n $ $ n $ n $时大大降低的。具体而言，在一个连贯时间或更短的分辨率下，求和序列的相对噪声差异会下降，$ n $作为$ 1/n $的增加（尽管（与辐射计方程相一致））当该系列平均储备平均为较低分辨率时，优势逐渐消失。因此，该算法旨在增强检测瞬态的灵敏度，这些瞬变是通过时间平均而平滑而淡淡的，在噪音无接触的时间序列中可见的，这证明了弱嵌入时间的嵌入式时间变化的信号，即定期性质或快速且无peated的脉搏。然后将算法应用于VLA对PULSAR PSR 1937+21的10分钟观察，其中通过数据验证了理论预测。此外，显示在时间配置文件中的微观结构可以更好地定义为使用的过滤器的数字$ n $增加，并且定期信号$ 1.86 \ times 10^{ - 5} $ 〜S（$ 53.9 $ 〜khz）在脉冲配置文件中发现。最后，我们将算法应用于Ligo，GW150914检测到的第一个二进制黑洞合并。我们发现平均峰强度的SNR随着$ \ sqrt {n} $的增加而增加，并且在Ligo-Hanford-Livingston检测器对之间的事件之间的跨相关性随滤波订单$ n $增加而增加。
The Gaussian phase noise of intensity time series is demonstrated to be drastically reduced when the raw voltage data are digitally filtered through an arbitrarily large number $n$ of orthornormal bandpass profiles (eigen-filters) sharing the same intensity bandwidth, and the resulting intensity series are co-added. Specifically, the relative noise variance of the summed series at the resolution of one coherence time or less, goes down with increasing $n$ as $1/n$, although (consistent with the radiometer equation) the advantage gradually disappears when the series is bin averaged to lower resolution. Thus the algorithm is designed to enhance the sensitivity of detecting transients that are smoothed out by time averaging and too faint to be visible in the noisy unaveraged time series, as demonstrated by the simulation of a weak embedded time varying signal of either a periodic nature or a fast and unrepeated pulse. The algorithm is then applied to a 10 minute observation of the pulsar PSR 1937+21 by the VLA, where the theoretical predictions were verified by the data. Moreover, it is shown that microstructures within the time profile are better defined as the number $n$ of filters used increases, and a periodic signal of period $1.86 \times 10^{-5}$~s ($53.9$~kHz) is discovered in the pulse profile. Lastly, we apply the algorithm to the first binary black hole merger detected by LIGO, GW150914. We find the SNR of the mean peak intensity increases as $\sqrt{n}$ and cross correlation of the event between the LIGO-Hanford-Livingston detector pair increases with filter order $n$.
