Challenges in space-based gravitational wave data analysis and applications of artificial intelligence

被引：3

作者：

Wang, He ^{[1
,2
]}

Du, MingHui ^{[3
]}

Xu, Peng ^{[2
,3
,4
,5
]}

Zhou, Yu-Feng ^{[6
]}

机构：

[1] Univ Chinese Acad Sci, Int Ctr Theoret Phys Asia Pacific, Beijing 100190, Peoples R China

[2] Univ Chinese Acad Sci UCAS, Taiji Lab Gravitat Wave Universe Beijing Hangzhou, Beijing 100049, Peoples R China

[3] Chinese Acad Sci, Inst Mech, Natl Micrograv Lab, Ctr Gravitat Wave Expt, Beijing 100190, Peoples R China

[4] Univ Chinese Acad Sci UCAS, Hangzhou Inst Adv Study, Hangzhou 310024, Peoples R China

[5] Lanzhou Univ, Lanzhou Ctr Theoret Phys, Lanzhou 730000, Peoples R China

[6] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China

来源：

SCIENTIA SINICA-PHYSICA MECHANICA & ASTRONOMICA | 2024年 / 54卷 / 07期

关键词：

gravitational-wave detection; data analysis; parameter estimation; artificial intelligence; BLACK-HOLE; BINARIES; SAMPLER; TIME; POPULATION; SIGNALS;

D O I：

10.1360/SSPMA-2024-0087

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

As space-based gravitational wave detection projects such as LISA, Taiji, and Tianqin continue to advance, we are on the cusp of gaining a new viewpoint on observing the universe. However, the scientific data processing for these projects faces unprecedented challenges, including the superposition of numerous gravitational wave sources, non-stationary noises, and data anomalies. This review aims to make a brief summary of these challenges and their possible solutions, using the Bayesian statistical inference framework as a thread, and provide researchers with a relatively comprehensive perspective. Topics such as the construction of waveform templates, the modeling of detector responses, and the processing of noise and data anomaly are discussed, with a focus on the strategies for parameter estimation and global fitting, especially the evaluation of likelihood, and the utilization of various stochastic sampling techniques to improve the efficiency and accuracy of analysis. Notably, this review highlights the applications of artificial intelligence technologies in waveform modeling, noise and data anomaly processing, signal recognition, and parameter estimation, showcasing how artificial intelligence can pave new paths for solving complex problems in the data analysis of space-based gravitational wave detection.

引用

页数：23

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