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Gravitational-wave surrogate models powered by artificial neural networks

Published:2021-03-10 Issue:6 Volume:103 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Khan Sebastian^ORCID,Green Rhys^ORCID

Funder

H2020 European Research Council

Science and Technology Facilities Council

University of Cambridge

Department for Business, Energy and Industrial Strategy

European Regional Development Fund

Welsh government

National Science Foundation

Cardiff University

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.103.064015/fulltext

Reference89 articles.

1. Accurate inspiral-merger-ringdown gravitational waveforms for nonspinning black-hole binaries including the effect of subdominant modes

2. Including mode mixing in a higher-multipole model for gravitational waveforms from nonspinning black-hole binaries

3. Including higher order multipoles in gravitational-wave models for precessing binary black holes

4. Enriching the symphony of gravitational waves from binary black holes by tuning higher harmonics

5. Time-domain effective-one-body gravitational waveforms for coalescing compact binaries with nonprecessing spins, tides, and self-spin effects

Cited by 31 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. Fast and faithful interpolation of numerical relativity surrogate waveforms using a meshfree approximation;Physical Review D;2024-09-10

2. Generating higher order modes from binary black hole mergers with machine learning;Physical Review D;2024-05-20

3. Probabilistic model for the gravitational wave signal from merging black holes;Physical Review D;2024-05-13

4. Gravitational-wave template banks for novel compact binaries;Physical Review D;2024-02-20

5. A Deep Learning Convolutional Neural Network for Antenna Near-Field Prediction and Surrogate Modeling;IEEE Access;2024

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