论文标题
深度学习连续重力候选者的聚类
Deep learning for clustering of continuous gravitational wave candidates
论文作者
论文摘要
在搜索非常多（$ \ 10^{17} $）模板上的连续重力波时，聚类是一种强大的工具，可以通过识别和捆绑由于同样的根本原因而提高搜索灵敏度。我们实施了一个深度学习网络，该网络可以在连续的重力波搜索输出中识别信号候选者的簇并评估其性能。对于大声的信号，我们的网络可实现高于97 \％的检测效率，错误警报率非常低，并保持了幅度较低的信号的合理检测效率，即以$ \ lyssim $ $ \ sillesim $当前的上限值。
In searching for continuous gravitational waves over very many ($\approx 10^{17}$) templates , clustering is a powerful tool which increases the search sensitivity by identifying and bundling together candidates that are due to the same root cause. We implement a deep learning network that identifies clusters of signal candidates in the output of continuous gravitational wave searches and assess its performance. For loud signals our network achieves a detection efficiency higher than 97\% with a very low false alarm rate, and maintains a reasonable detection efficiency for signals with lower amplitudes, i.e. at $\lesssim$ current upper limit values.
