Advancing space-based gravitational wave astronomy: Rapid parameter estimation via normalizing flows  

作　　者：Minghui Du Bo Liang He Wang Peng Xu Ziren Luo Yueliang Wu 

机构地区：[1]Center for Gravitational Wave Experiment,National Microgravity Laboratory,Institute of Mechanics,Chinese Academy of Sciences,Beijing 100190,China [2]Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province,Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Hangzhou 310024,China [3]Shanghai Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Shanghai 201800,China [4]International Centre for Theoretical Physics Asia-Pacific,University of Chinese Academy of Sciences,Beijing 100049,China [5]Taiji Laboratory for Gravitational Wave Universe(Beijing/Hangzhou),University of Chinese Academy of Sciences,Beijing 100049,China [6]Lanzhou Center of Theoretical Physics,Lanzhou University,Lanzhou 730000,China [7]Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Hangzhou 310024,China [8]Institute of Theoretical Physics,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2024年第3期20-33,共14页中国科学：物理学、力学、天文学（英文版）

基　　金：supported by the National Key Research and Development Program of China (Grant Nos. 2021YFC2203004, and 2021YFC2201903);supported by the National Natural Science Foundation of China (Grant Nos. 12147103, and 12247187);the Fundamental Research Funds for the Central Universities。

摘　　要：Gravitational wave(GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries(MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise.Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation.

关 键 词：Taiji program gravitational wave detection parameter estimation machine learning 

分 类 号：P142.84[天文地球—天体物理]