论文标题
使用机器学习的引力波的定位
Localization of gravitational waves using machine learning
论文作者
论文摘要
对引力波的观察是对天文事件的多通间搜索的触发因素。来自两个或三个引力波望远镜的数据组合表明源的位置和低延迟数据分析是将信息传输到不同波长敏感的其他望远镜敏感的关键。与依赖匹配过滤技术的当前方法相反，我们提出了使用机器学习的使用，该方法比匹配的过滤更快，更准确。我们的机器学习方法是Chatterjee {\ it等}提出的方法的组合，以及使用时间卷积网络的方法。我们使用四个望远镜（Ligo H1，Ligo L1，处女座和Kagra）展示了引力波源的天空定位，并在没有Kagra的情况下比较了该案例的结果，以检查在全球重力波网络中拥有第四台望远镜的积极影响。
An observation of gravitational waves is a trigger of the multi-messenger search of an astronomical event. A combination of the data from two or three gravitational wave telescopes indicates the location of a source and low-latency data analysis is key to transferring the information to other telescopes sensitive at different wavelengths. In contrast to the current method, which relies on the matched-filtering technique, we proposed the use of machine learning that is much faster and possibly more accurate than matched filtering. Our machine-learning method is a combination of the method proposed by Chatterjee {\it et al.} and a method using the temporal convolutional network. We demonstrate the sky localization of a gravitational-wave source using four telescopes: LIGO H1, LIGO L1, Virgo, and KAGRA, and compare the result in the case without KAGRA to examine the positive influence of having the fourth telescope in the global gravitational-wave network.
