论文标题
深度学习连续重力波候选物的聚类ii：识别低SNR候选者
Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates
论文作者
论文摘要
对连续重力波信号的广泛搜索依赖于高于给定显着性阈值的候选者的后续阶段的层次结构。简化这些随访并降低计算成本的重要步骤是在附近候选人的单个后续行动中捆绑在一起。此步骤称为聚类，我们调查使用深度学习网络进行。在我们的第一篇论文[1]中，我们实施了一个深度学习聚类网络，能够由于大型信号而正确识别群集。在本文中，实现了一个网络，该网络可以由于许多薄弱的信号而检测群集。这两个网络是互补的，我们表明两个网络的级联在广泛的信号强度上达到了出色的检测效率，错误的警报速率可比/低于当前使用的方法。
Broad searches for continuous gravitational wave signals rely on hierarchies of follow-up stages for candidates above a given significance threshold. An important step to simplify these follow-ups and reduce the computational cost is to bundle together in a single follow-up nearby candidates. This step is called clustering and we investigate carrying it out with a deep learning network. In our first paper [1], we implemented a deep learning clustering network capable of correctly identifying clusters due to large signals. In this paper, a network is implemented that can detect clusters due to much fainter signals. These two networks are complementary and we show that a cascade of the two networks achieves an excellent detection efficiency across a wide range of signal strengths, with a false alarm rate comparable/lower than that of methods currently in use.
