论文标题
深度卷积神经网络的合奏，用于实时重力波信号识别
Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition
论文作者
论文摘要
随着深度学习技术的快速发展，越来越多的研究人员将其应用于引力波（GW）数据分析。先前的研究集中在一个深度学习模型上。在本文中，我们设计了一种合并一组卷积神经网络（CNN）的集成算法，用于GW信号识别。整个集合模型由两个子集合模型组成。每个子集结模型也是深度学习的整体模型。两个子集结模型分别处理汉福德和利文斯顿检测器的数据。采用适当的投票方案来组合两个子集团模型以形成整个整体模型。我们将此集合模型应用于第一次观察中所有报道的GW事件，以及Ligo-Virgo Scientific Collaporation的第二次观察（O1/O2）。我们发现集合算法可以清楚地识别除GW170818以外的所有二进制黑洞合并事件。我们还将合奏模型应用于O2的一个月（2017年8月）数据。尽管仅使用O1数据进行培训，但没有发生错误的触发因素。我们的测试结果表明，集成学习算法可用于实时GW数据分析。
With the rapid development of deep learning technology, more and more researchers apply it to gravitational wave (GW) data analysis. Previous studies focused on a single deep learning model. In this paper we design an ensemble algorithm combining a set of convolutional neural networks (CNN) for GW signal recognition. The whole ensemble model consists of two sub-ensemble models. Each sub-ensemble model is also an ensemble model of deep learning. The two sub-ensemble models treat data of Hanford and Livinston detectors respectively. Proper voting scheme is adopted to combine the two sub-ensemble models to form the whole ensemble model. We apply this ensemble model to all reported GW events in the first observation and second observation runs (O1/O2) by LIGO-VIRGO Scientific Collaboration. We find that the ensemble algorithm can clearly identify all binary black hole merger events except GW170818. We also apply the ensemble model to one month (August 2017) data of O2. There is no false trigger happens although only O1 data are used for training. Our test results indicate that the ensemble learning algorithms can be used in real-time GW data analysis.
