Improved deep learning techniques in gravitational-wave data analysis-Reference-Cited by-同舟云学术

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Improved deep learning techniques in gravitational-wave data analysis

Published:2021-01-21 Issue:2 Volume:103 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Xia Heming^ORCID,Shao Lijing^ORCID,Zhao Junjie^ORCID,Cao Zhoujian^ORCID

Funder

National Natural Science Foundation of China

China Association for Science and Technology

Max-Planck-Gesellschaft

Peking University

Chinese Academy of Sciences

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.103.024040/fulltext

Reference65 articles.

1. Observation of Gravitational Waves from a Binary Black Hole Merger

2. GW150914 and gravitational-wave astronomy

3. GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs

4. 1-OGC: The First Open Gravitational-wave Catalog of Binary Mergers from Analysis of Public Advanced LIGO Data

Cited by 20 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. Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences;Physical Review D;2024-02-07

4. Using deep learning to predict matched signal-to-noise ratio of gravitational waves;Physical Review D;2024-02-05

5. Accuracy of numerical relativity waveforms with respect to space-based gravitational wave detectors;Communications in Theoretical Physics;2024-01-01

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