论文标题
对中子星星合并的重力波的深度学习
Deep learning for gravitational wave forecasting of neutron star mergers
论文作者
论文摘要
我们介绍了深度学习时间序列预测，以预测二进制中子星星合并的重力波检测。此方法可以在合并前30秒内在实际高级LIGO数据中识别这些信号。当应用于GW170817时，我们的深度学习预测方法可以在合并前10秒确定该引力波信号的存在。这种新颖的方法需要一个单一的GPU进行推理，可以用作时间敏感的多理性搜索的预警系统的一部分。
We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches.
