Including mode mixing in a higher-multipole model for gravitational waveforms from nonspinning black-hole binaries-Reference-Cited by-同舟云学术

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

Published:2019-07-16 Issue:2 Volume:100 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Kumar Mehta Ajit^ORCID,Tiwari Praveer,Johnson-McDaniel Nathan K.,Mishra Chandra Kant,Varma Vijay,Ajith Parameswaran

Funder

Indo-US Science and Technology Forum

Science and Technology Facilities Council

H2020 Marie Skłodowska-Curie Actions

Max-Planck-Gesellschaft

Canadian Institute for Advanced Research

Sherman Fairchild Foundation

National Science Foundation

AIRBUS Group Corporate Foundation

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.100.024032/fulltext

Reference68 articles.

1. Advanced LIGO

2. Advanced Virgo: a second-generation interferometric gravitational wave detector

3. Properties of the Binary Black Hole Merger GW150914

4. Directly comparing GW150914 with numerical solutions of Einstein’s equations for binary black hole coalescence

5. Parameter estimation method that directly compares gravitational wave observations to numerical relativity

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1. Measuring source properties and quasinormal mode frequencies of heavy massive black-hole binaries with LISA;Physical Review D;2024-05-06

2. Laying the foundation of the effective-one-body waveform models SEOBNRv5: Improved accuracy and efficiency for spinning nonprecessing binary black holes;Physical Review D;2023-12-15

3. Deep residual error and bag-of-tricks learning for gravitational wave surrogate modeling;Applied Soft Computing;2023-11

4. Biorthogonal harmonics for the decomposition of gravitational radiation. I. Angular modes, completeness, and the introduction of adjoint-spheroidal harmonics;Physical Review D;2023-02-22

5. Autoencoder-driven spiral representation learning for gravitational wave surrogate modelling;Neurocomputing;2022-06

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