论文标题
使用深度学习从紧凑型二元合并的重力波源快速定位
Rapid localization of gravitational wave sources from compact binary coalescences using deep learning
论文作者
论文摘要
与电磁对应物相对的最有前途的重力波事件，中子星形中子恒星和中子星形黑孔的合并是最有希望的引力事件。在Ligo-Virgo-Kagra合作的即将到来的科学运行中，这些来源的快速检测，本地化和同时进行的多理智随访至关重要。虽然二进制合并期间的迅速电磁对应物可以持续不到两秒钟，但使用贝叶斯技术的现有定位方法的时间尺度在几秒钟之间变化。在本文中，我们提出了第一种基于深度学习的方法，用于在所有类型的二元聚合中快速，准确的天空定位，包括首次中子星形恒星和中子星形黑色霍格二进制。具体而言，我们在重力波搜索的匹配过滤输出上训练并测试了归一化流量模型。我们的模型使用单个P100 GPU以毫秒为单位生产天空方向，该p100 GPU比贝叶斯技术快三到六个数量级。
The mergers of neutron star-neutron star and neutron star-black hole binaries are the most promising gravitational wave events with electromagnetic counterparts. The rapid detection, localization and simultaneous multi-messenger follow-up of these sources is of primary importance in the upcoming science runs of the LIGO-Virgo-KAGRA Collaboration. While prompt electromagnetic counterparts during binary mergers can last less than two seconds, the time scales of existing localization methods that use Bayesian techniques, varies from seconds to days. In this paper, we propose the first deep learning-based approach for rapid and accurate sky localization of all types of binary coalescences, including neutron star-neutron star and neutron star-black hole binaries for the first time. Specifically, we train and test a normalizing flow model on matched-filtering output from gravitational wave searches. Our model produces sky direction posteriors in milliseconds using a single P100 GPU, which is three to six orders of magnitude faster than Bayesian techniques.
