Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy
Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy
复制标题
DOI：
10.1103/physrevlett.120.141103
复制
发表时间：
2018-04-06
影响因子：
8.6
通讯作者：
Messenger, Chris
中科院分区：
文献类型：
--
作者：
Gabbard, Hunter;Williams, Michael;Messenger, Chris
我们报告了一个深度卷积神经网络的构建，该网络可以重现对二元黑洞引力波信号进行匹配滤波搜索的灵敏度。检测精确建模的瞬态重力波信号的标准方法是匹配滤波。我们只使用测得的引力波应变的白化时间序列作为输入，并在代表高级LIGO灵敏度的合成高斯噪声中对模拟的二元黑洞信号进行训练和测试。我们表明，我们的网络可以分类信号与噪声的性能，模仿匹配滤波应用于相同的数据集时，考虑接收器操作员的特性定义的灵敏度。
We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.