In this paper, we report on the construction of a deep Artificial Neural Network (ANN) to localize simulated gravitational wave signals in the sky with high accuracy. We have modelled the sky as a sphere and have considered cases where the sphere is divided into 18, 50, 128, 1024, 2048 and 4096 sectors. The sky direction of the gravitational wave source is estimated by classifying the signal into one of these sectors based on it's right ascension and declination values for each of these cases. In order to do this, we have injected simulated binary black hole gravitational wave signals of component masses sampled uniformly between 30-80 solar mass into Gaussian noise and used the whitened strain values to obtain the input features for training our ANN. We input features such as the delays in arrival times, phase differences and amplitude ratios at each of the three detectors Hanford, Livingston and Virgo, from the raw time-domain strain values as well as from analytical versions of these signals, obtained through Hilbert transformation. We show that our model is able to classify gravitational wave samples, not used in the training process, into their correct sectors with very high accuracy (>90%) for coarse angular resolution using 18, 50 and 128 sectors. We also test our localization on test samples with injection parameters of the published LIGO binary black hole merger events GW150914, GW170818 and GW170823 for 1024, 2048 and 4096 sectors and compare the result with that from BAYESTAR and Parameter Estimation (PE). In addition, we report that the time taken by our model to localize one GW signal is around 0.018 secs on 14 Intel Xeon CPU cores.
Bayesian Framework to Follow-up Continuous Gravitational Wave Candidates from Deep Surveys
Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（爱因斯坦研究所））；Leibniz Universität Hannover（汉诺威莱布尼茨大学）；Departament de Física, Universitat de les Illes Balears, IAC3 – IEEC（巴利阿里群岛大学物理系，IAC3-IEEC）
Results of the deepest all-sky survey for continuous gravitational waves on LIGO S6 data running on the Einstein@Home volunteer distributed computing project
DeepSNR: A deep learning foundation for offline gravitational wave detection
Carnegie Mellon University（卡内基梅隆大学）；Advanced Propulsion Laboratory at Applied Physics（应用物理高级推进实验室）；Lund University（隆德大学）；Technion - Israel Institute of Technology（以色列理工学院）
Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
实时探测和估计致密双星合并产生的引力波的关键挑战之一是传统的匹配滤波和贝叶斯推断方法的计算成本。特别是，将这些方法应用于引力波探测器可用的完整信号参数空间和/或实时参数估计在计算上是禁止的。另一方面，快速探测和推断对于迅速跟踪伴随重要瞬变的电磁和天体粒子对应物，如中子星双星和黑洞中子星合并，至关重要。训练深度神经网络来识别特定信号并学习引力波信号及其参数之间映射的计算有效表示，可以快速可靠地进行检测和推断，具有高灵敏度和准确性。在这项工作中，我们应用了一种深度学习方法来快速识别和表征真实LIGO数据中中子星合并的瞬态引力波信号。我们第一次证明了人工神经网络可以及时检测和表征真实LIGO数据中的二进制中子星引力波信号，并将其与噪声和合并黑洞二进制信号区分开来。我们通过证明我们的深度学习框架正确地分类了来自引力波瞬态目录GWTC-1的所有引力波事件来说明这一关键结果[Phys. Rev. X 9（2019），031040]。这些结果强调了在机器学习方法中使用真实引力波探测器数据的重要性，并代表了实现引力波实时探测和推断的一步。
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
NCSA, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校国家超级计算应用中心）；Department of Statistics, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校统计系）；Department of Astronomy, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校天文系）；Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校电气与计算机工程系）
