标题： 深度残差网络用于引力波检测 显示英文标题

标题： Deep Residual Networks for Gravitational Wave Detection

摘要： 传统上，引力波是通过匹配滤波或基于小波的无模型搜索等技术检测的。 然而，在具有非对齐自旋的一般黑洞双星情况下，如果想要探索整个参数空间，匹配滤波可能变得不切实际，这会对引力波搜索的灵敏度和计算效率设置严重限制。 在这里，我们使用了一种机器学习算法的新组合，并在特定设置中实现了超越传统技术的灵敏距离。 此外，计算成本仅为匹配滤波计算成本的一小部分。 主要组件包括一个54层的深度残差网络（ResNet）、深度自适应输入归一化（DAIN）、动态数据集增强和课程学习，这些基于信噪比的经验关系。 我们在包含大量注入波形的的数据集上将该算法的灵敏度与两种传统算法进行了比较，这些波形是非对齐双黑洞合并的真实LIGO O3a噪声样本中的波形。 我们的机器学习算法可用于未来在天体物理上有兴趣的参数空间的较大区域内快速在线搜索引力波事件。 我们已在https://github.com/vivinousi/gw-detection-deep-learning 公开提供我们的代码AResGW和详细结果。 显示英文摘要

摘要： Traditionally, gravitational waves are detected with techniques such as matched filtering or unmodeled searches based on wavelets. However, in the case of generic black hole binaries with non-aligned spins, if one wants to explore the whole parameter space, matched filtering can become impractical, which sets severe restrictions on the sensitivity and computational efficiency of gravitational-wave searches. Here, we use a novel combination of machine-learning algorithms and arrive at sensitive distances that surpass traditional techniques in a specific setting. Moreover, the computational cost is only a small fraction of the computational cost of matched filtering. The main ingredients are a 54-layer deep residual network (ResNet), a Deep Adaptive Input Normalization (DAIN), a dynamic dataset augmentation, and curriculum learning, based on an empirical relation for the signal-to-noise ratio. We compare the algorithm's sensitivity with two traditional algorithms on a dataset consisting of a large number of injected waveforms of non-aligned binary black hole mergers in real LIGO O3a noise samples. Our machine-learning algorithm can be used in upcoming rapid online searches of gravitational-wave events in a sizeable portion of the astrophysically interesting parameter space. We make our code, AResGW, and detailed results publicly available at https://github.com/vivinousi/gw-detection-deep-learning .