标题： 深度学习用于LIGO数据的实时引力波探测和参数估计 显示英文标题

标题： Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data

摘要： 近年来，通过合并黑洞探测引力波并随后与电磁观测同时探测中子星碰撞的诺贝尔奖获奖发现，开启了多信使天体物理学的新时代。 为了拓展这门新兴科学的范围，我们提出了使用深度卷积神经网络在实时情况下检测和表征引力波信号。 这种方法，Deep Filtering，最初是使用模拟的LIGO噪声进行演示的。 在本文中，我们展示了使用LIGO第一次观测运行的真实数据对Deep Filtering的扩展，用于从多个LIGO探测器的连续数据流中检测和参数估计双黑洞合并的引力波。 我们首次表明机器学习可以检测并估计由LIGO观测到的真实GW事件的真实参数。 我们的比较显示，Deep Filtering比匹配滤波更计算高效，同时保持相似的灵敏度和更低的误差，允许在非平稳非高斯噪声中以最小资源实时处理微弱的时间序列信号，并且还能检测现有检测算法可能忽略的新类别的引力波源。 这种方法特别适合实现实时的引力波及其多信使对应物的协同检测活动。 显示英文摘要

摘要： 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, we proposed the use of deep convolutional neural networks for the detection and characterization of gravitational wave signals in real-time. This method, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data 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 sensitivity and lower errors, 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 approach is uniquely suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.