标题： 利用深度学习检测和降噪双黑洞引力波信号 显示英文标题

标题： Detecting and Denoising Gravitational Wave Signals from Binary Black Holes using Deep Learning

摘要： 我们提出了一种卷积神经网络，设计为自编码器配置，能够检测和去噪由合并黑洞双星产生的天体现象引力波，其速度比目前高级激光干涉引力波天文台（aLIGO）采用的传统基于匹配滤波的检测方法快几个数量级。 神经网络架构使得它能够从时频域数据的稀疏表示中学习，并构建一个非线性映射函数，将这种表示映射到信号和噪声的两个独立掩码上，从而从原始数据中分离这两种成分。 这种方法首次将基于机器学习的引力波检测/去噪应用于引力波数据的二维表示中。 我们将该方法应用于首次探测到的引力波事件GW150914，在两个探测器的所有三个并合阶段成功恢复了信号。 此方法还在aLIGO第二次观测运行（$O2$）的引力波数据上进行了进一步测试，在两个aLIGO探测器上重现了在$O2$中检测到的所有双黑洞并合事件。 神经网络似乎揭示了在并合后回响阶段之后的一种“回响”模式，这不是传统二元并合模板中存在的特征。 该方法还可以在模型模板之间进行插值和外推，探索未建模因而不在匹配滤波检测管道使用的信号模板库中的引力波。 随着地面探测器达到设计灵敏度，像这种方法这样更快、更高效的检测方案将起到关键作用，可能会在几个月的观测运行中产生数百个潜在的检测结果。 显示英文摘要

摘要： We present a convolutional neural network, designed in the auto-encoder configuration that can detect and denoise astrophysical gravitational waves from merging black hole binaries, orders of magnitude faster than the conventional matched-filtering based detection that is currently employed at advanced LIGO (aLIGO). The Neural-Net architecture is such that it learns from the sparse representation of data in the time-frequency domain and constructs a non-linear mapping function that maps this representation into two separate masks for signal and noise, facilitating the separation of the two, from raw data. This approach is the first of its kind to apply machine learning based gravitational wave detection/denoising in the 2D representation of gravitational wave data. We applied our formalism to the first gravitational wave event detected, GW150914, successfully recovering the signal at all three phases of coalescence at both detectors. This method is further tested on the gravitational wave data from the second observing run ($O2$) of aLIGO, reproducing all binary black hole mergers detected in $O2$ at both the aLIGO detectors. The Neural-Net seems to have uncovered a pattern of 'ringing' after the ringdown phase of the coalescence, which is not a feature that is present in the conventional binary merger templates. This method can also interpolate and extrapolate between modeled templates and explore gravitational waves that are unmodeled and hence not present in the template bank of signals used in the matched-filtering detection pipelines. Faster and efficient detection schemes, such as this method, will be instrumental as ground based detectors reach their design sensitivity, likely to result in several hundreds of potential detections in a few months of observing runs.