Deep learning for intermittent gravitational wave signals-Reference-Cited by-同舟云学术

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Deep learning for intermittent gravitational wave signals

Published:2023-02-16 Issue:4 Volume:107 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Yamamoto Takahiro S.^ORCID,Kuroyanagi Sachiko,Liu Guo-Chin^ORCID

Funder

Japan Society for the Promotion of Science

Comunidad de Madrid

Agencia Estatal de Investigación

Consejo Superior de Investigaciones Científicas

Ministry of Science and Technology, Taiwan

Spanish Research Agency

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.107.044032/fulltext

Reference38 articles.

1. GW150914: Implications for the Stochastic Gravitational-Wave Background from Binary Black Holes

2. GW170817: Implications for the Stochastic Gravitational-Wave Background from Compact Binary Coalescences

3. Upper limits on the isotropic gravitational-wave background from Advanced LIGO and Advanced Virgo’s third observing run

4. Detection methods for non-Gaussian gravitational wave stochastic backgrounds

5. Measuring the non-Gaussian stochastic gravitational-wave background: A method for realistic interferometer data

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using deep learning to denoise and detect gravitational waves;Physical Review D;2024-09-06

2. Premerger detection of massive black hole binaries using deep learning;Physical Review D;2024-06-24

3. A stochastic search for intermittent gravitational-wave backgrounds;Physical Review D;2023-05-15

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