Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers-Reference-Cited by-同舟云学术

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Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers

Published:2022-11-10 Issue:10 Volume:106 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Mould Matthew^ORCID,Gerosa Davide^ORCID,Taylor Stephen R.

Funder

H2020 European Research Council

Fondazione Cariplo

Leverhulme Trust

Royal Society

European Cooperation in Science and Technology

National Science Foundation

Engineering and Physical Sciences Research Council

Instituto Nazionale di Fisica Nucleare

ISCRA

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.106.103013/fulltext

Reference139 articles.

1. Advanced LIGO

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

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. GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo during the First Half of the Third Observing Run

5. 3-OGC: Catalog of Gravitational Waves from Compact-binary Mergers

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1. Inferring the spin distribution of binary black holes using deep learning;Chinese Physics C;2024-10-01

2. Resolving the Stellar-Collapse and Hierarchical-Merger Origins of the Coalescing Black Holes;Physical Review Letters;2024-08-01

3. Mass ratio of binary black holes determined from LIGO/Virgo data restricted to small false alarm rate;Monthly Notices of the Royal Astronomical Society;2024-06-14

4. Evidence of evolution of the black hole mass function with redshift;Astronomy & Astrophysics;2024-04

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