▲
作者:Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, et al.
▲ 链接:
https://www.nature.com/articles/s41586-025-08593-z
▲ 摘要:
双中子星的合并会同时发射引力波(GW)和电磁波谱信号。众所周知,2017年对GW170817的多信使观测导致了宇宙学、核物理学和引力领域的科学发现。这些结果的核心是从GW数据(如GW170817)中获得的天空定位和距离,这有助于识别GW信号发出后11小时的相关电磁瞬变,即AT 2017gfo。
快速分析GW数据对于指导时间敏感的电磁观测至关重要。然而,由于信号长度和复杂性带来的挑战,通常需要做出牺牲精度的近似值。
研究组提出了一个机器学习框架,可以在短短1秒内执行完整的双中子星推理,而无需引入近似假设。该方法通过提供以下优势来增强多信使观测:(1)即使在合并之前也能精确定位;(2)与近似低延迟方法相比,定位精度提高30%左右;(3)详细的光度距离、倾角和质量信息,可用来优先考虑昂贵的望远镜时间。
此外,该方法的灵活性和降低的成本为状态方程研究开辟了新的机会。最后,研究组证明了该方法可扩展到长达一小时的长信号,从而为下一代地面和空基探测器数据分析提供了蓝图。
▲ Abstract:
Mergers of binary neutron stars emit signals in both the gravitational-wave (GW) and electromagnetic spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics and gravity. Central to these results were the sky localization and distance obtained from the GW data, which, in the case of GW170817, helped to identify the associated electromagnetic transient, AT 2017gfo, 11 h after the GW signal. Fast analysis of GW data is critical for directing time-sensitive electromagnetic observations. However, owing to challenges arising from the length and complexity of signals, it is often necessary to make approximations that sacrifice accuracy. Here we present a machine-learning framework that performs complete binary neutron star inference in just 1 s without making any such approximations. Our approach enhances multi-messenger observations by providing: (1) accurate localization even before the merger; (2) improved localization precision by around 30% compared to approximate low-latency methods; and (3) detailed information on luminosity distance, inclination and masses, which can be used to prioritize expensive telescope time. Additionally, the flexibility and reduced cost of our method open new opportunities for equation-of-state studies. Finally, we demonstrate that our method scales to long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors.