Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State

Qian Hu, Jessica Irwin, Qi Sun, Christopher Messenger, Lami Suleiman, Ik Siong Heng and John Veitch
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Abstract

Gravitational waves (GWs) from binary neutron stars (BNSs) offer a valuable understanding of the nature of compact objects and hadronic matter, and the science potential will be greatly enhanced by the third-generation (3G) GW detectors, which are expected to detect BNS signals with order-of-magnitude improvements in duration, detection rates, and signal strength. However, the resulting computational demands for analyzing such prolonged signals pose a critical challenge that existing Bayesian methods cannot feasibly address in the 3G era. To bridge this critical gap, we demonstrate a machine learning–based workflow capable of producing source parameter estimation and constraints on equations of state (EOSs) for hours-long BNS signals in seconds with minimal hardware costs. We employ efficient compression of the GW data and EOS using neural networks, based on which we build normalizing flows for inference that can deliver results in seconds. The optimized computational cost of BNS signal analysis with our framework shows that machine learning has the potential to be an indispensable tool for future catalog-level BNS analyses, paving the way for large-scale investigations of BNS-related physics across the 3G observational landscape.
用机器学习解码来自双中子星的长时间引力波:参数估计和状态方程
来自双中子星(BNS)的引力波(GWs)提供了对致密物体和强子物质本质的宝贵理解,第三代(3G) GW探测器将极大地增强科学潜力,它们有望在持续时间、探测率和信号强度方面实现数量级的提高。然而,分析这种长时间信号所产生的计算需求提出了一个关键的挑战,现有的贝叶斯方法在3G时代无法切实解决。为了弥补这一关键差距,我们展示了一种基于机器学习的工作流程,能够在几秒钟内以最小的硬件成本为长达数小时的BNS信号生成源参数估计和状态方程(EOSs)约束。我们使用神经网络对GW数据和EOS进行有效压缩,在此基础上,我们构建了规范化的推理流,可以在几秒钟内提供结果。利用我们的框架优化了BNS信号分析的计算成本,这表明机器学习有可能成为未来目录级BNS分析不可或缺的工具,为3G观测领域中BNS相关物理的大规模研究铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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