The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent science, the use of deep convolutional neural networks were proposed for the detection and characterization of gravitational wave signals in real-time. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real noise from the first observing run of LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers with continuous data streams from multiple LIGO detectors. We show for the first time that machine learning can detect and estimate the true parameters of a real GW event observed by LIGO. Our comparisons show that Deep Filtering is far more computationally efficient than matched-filtering, while retaining similar performance, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise, with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This framework is uniquely suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.

该论文介绍了一种名为Deep Filtering的新方法，使用深度卷积神经网络可以在实时监测中检测和表征引力波信号，这是一种比传统的Matched Filtering方法更为高效的方法，通过对LIGO数据的实时处理，可以实现对弱时间序列信号的检测，并且可以探测新型引力波源。

LIGO数据实时引力波探测与参数估计的深度学习