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Search for exotic gravitational wave signals beyond general relativity using deep learning

文献类型：期刊论文

作者Wang, YuXin7,8; Wei XT(魏晓通)6; Li, ChunYue7,8; Sun, TianYang7,8; Jin, ShangJie5,7,8; Wang, He3,4; Cui, JingLei7,8; Zhang, JingFei7,8; Zhang, Xin1,2,7,8
刊名PHYSICAL REVIEW D
出版日期2025-07-09
卷号112期号:2页码:13
ISSN号2470-0010
DOI10.1103/f85k-wtph
通讯作者Wang, He(hewang@ucas.ac.cn) ; Zhang, Xin(zhangxin@mail.neu.edu.cn)
英文摘要The direct detection of gravitational waves by LIGO has confirmed Einstein's general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newtonian (PN) deviations observed during the analysis of high signal-to-noise ratio events from the observational runs suggest that standard waveform templates, which assume strict adherence to GR, might overlook signals from alternative theories of gravity. Incorporating these exotic signals into traditional search algorithms is computationally infeasible due to the vast template space required. This paper introduces a proof-of-principle deep learning framework for detecting exotic GW signals, leveraging neural networks trained on GR-based templates. Through their generalization ability, neural networks learn intricate features from the data, enabling the detection of signals that deviate from GR. We present the first study evaluating the capability of deep learning to detect beyond-GR signals, including a variety of PN orders. Our model achieves rapid and accurate identification of exotic GW signals across different luminosity distances, with performance comparable to GR-based detections. In particular, applying the model to the GW150914 event demonstrates excellent performance, highlighting the potential of AI-driven methods for detecting previously overlooked signals beyond GR. This work paves the way for new discoveries in gravitational wave astronomy, enabling the detection of signals that might escape traditional search pipelines.
分类号二类/Q1
WOS关键词HUBBLE CONSTANT ; DARK ENERGY ; BLACK-HOLES ; PARAMETER-ESTIMATION ; OBSERVING RUN ; SIRENS ; PROSPECTS ; GRAVITY ; TENSOR ; LIGO
资助项目National Natural Science Foundation of China[12473001] ; National Natural Science Foundation of China[11975072] ; National Natural Science Foundation of China[11875102] ; National Natural Science Foundation of China[11835009] ; National Natural Science Foundation of China[12405076] ; National Natural Science Foundation of China[12347103] ; National SKA Program of China[2022SKA0110200] ; National SKA Program of China[2022SKA0110203] ; The 111 Project[B16009]
WOS研究方向Astronomy & Astrophysics ; Physics
语种英语
WOS记录号WOS:001529656800006
资助机构National Natural Science Foundation of China ; National SKA Program of China ; The 111 Project
其他责任者Wang, He，Zhang, Xin
源URL[http://dspace.imech.ac.cn/handle/311007/102062]  
专题力学研究所_国家微重力实验室
作者单位1.Northeastern Univ, Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt, Shenyang 110819, Peoples R China
2.Northeastern Univ, Key Lab Data Analyt & Optimizat Smart Ind, Minist Educ, Shenyang 110819, Peoples R China;
3.Univ Chinese Acad Sci, Taiji Lab Gravitat Wave Universe Beijing Hangzhou, Beijing 100190, Peoples R China;
4.Univ Chinese Acad Sci, Int Ctr Theoret Phys Asia Pacific, Beijing 100190, Peoples R China;
5.Univ Western Australia, Dept Phys, Perth, WA 6009, Australia;
6.Chinese Acad Sci, Ctr Gravitat Wave Expt, Inst Mech, Beijing 100190, Peoples R China;
7.Northeastern Univ, Coll Sci, Shenyang 110819, Peoples R China;
8.Northeastern Univ, Key Lab Cosmol & Astrophys Liaoning, Shenyang 110819, Peoples R China;
推荐引用方式
GB/T 7714		 Wang, YuXin,Wei XT,Li, ChunYue,et al. Search for exotic gravitational wave signals beyond general relativity using deep learning[J]. PHYSICAL REVIEW D,2025,112(2):13.
APA Wang, YuXin.,魏晓通.,Li, ChunYue.,Sun, TianYang.,Jin, ShangJie.,...&Zhang, Xin.(2025).Search for exotic gravitational wave signals beyond general relativity using deep learning.PHYSICAL REVIEW D,112(2),13.
MLA Wang, YuXin,et al."Search for exotic gravitational wave signals beyond general relativity using deep learning".PHYSICAL REVIEW D 112.2(2025):13.

入库方式： OAI收割

来源：力学研究所

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