In this paper, we study an application of deep learning to the advanced laser interferometer gravitational wave observatory (LIGO) and advanced Virgo coincident detection of gravitational waves (GWs) from compact binary star mergers. This deep learning method is an extension of the Deep Filtering method used by George and Huerta (2017) for multi-inputs of network detectors. Simulated coincident time series data sets in advanced LIGO and advanced Virgo detectors are analyzed for estimating source luminosity distance and sky location. As a classifier, our deep neural network (DNN) can effectively recognize the presence of GW signals when the optimal signal-to-noise ratio (SNR) of network detectors ≥ 9. As a predictor, it can also effectively estimate the corresponding source space parameters, including the luminosity distance D, right ascension α, and declination δ of the compact binary star mergers. When the SNR of the network detectors is greater than 8, their relative errors are all less than 23%. Our results demonstrate that Deep Filtering can process coincident GW time series inputs and perform effective classification and multiple space parameter estimation. Furthermore, we compare the results obtained from one, two, and three network detectors; these results reveal that a larger number of network detectors results in a better source location.

中文翻译：

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深度神经网络在重力波检测器网络的紧凑型二元合并检测和空间参数估计中的应用

在本文中，我们研究了深度学习在高级激光干涉仪引力波天文台（LIGO）和紧凑型双星合并中对重力波（GWs）进行高级处女座重合检测的应用。这种深度学习方法是George and Huerta（2017）用于网络检测器多输入的深度过滤方法的扩展。分析了高级LIGO和高级处女座探测器中模拟的重合时间序列数据集，以估算源光度距离和天空位置。作为分类器，当网络检测器的最佳信噪比（SNR）≥9时，我们的深度神经网络（DNN）可以有效识别GW信号的存在。作为预测器，它也可以有效地估计相应的源空间参数，包括光度距离D，右提升α，紧凑双星合并的δ。当网络检测器的SNR大于8时，它们的相对误差都小于23％。我们的结果表明，深度过滤可以处理重合的GW时间序列输入并执行有效的分类和多个空间参数估计。此外，我们比较了从一个，两个和三个网络检测器获得的结果；这些结果表明，大量的网络检测器可以提供更好的源位置。