The Targeted Detectability Range (TDR) incorporates sky localization, inclination constraints, and mass bounds from external messengers to evaluate gravitational-wave detectability for gamma-ray bursts observed during LIGO-Virgo-KAGRA's first three runs.
2019 a , , 100, 044003, 10.1103/PhysRevD.100.044003
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
verdicts
UNVERDICTED 7representative citing papers
New simulations show that cross-correlating gravitational wave background anisotropies with galaxy distributions can enable discovery at angular scales of 4-6 degrees with next-generation observatories.
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
No coincident GW signals found with long GRBs in O3 run; luminosity distance limits set assuming binary merger powering.
Describes the methods for producing the fifth gravitational-wave transient catalog (GWTC-5.0) from O4b data of LIGO, Virgo and KAGRA.
BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.
This review summarizes tidal Love numbers and dissipation effects for black holes, neutron stars, and exotic objects, noting vanishing static bosonic Love numbers for black holes in GR but nonzero values for fermions and exotic objects, with implications for gravitational-wave astronomy.
citing papers explorer
-
Gravitational wave detectability range informed by external messengers
The Targeted Detectability Range (TDR) incorporates sky localization, inclination constraints, and mass bounds from external messengers to evaluate gravitational-wave detectability for gamma-ray bursts observed during LIGO-Virgo-KAGRA's first three runs.
-
Reconstruction of fast-rotating neutron star observables with the neural network
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
-
Searching for gravitational waves from compact binary mergers powering long gamma-ray bursts during LIGO-Virgo-KAGRA's O3 run
No coincident GW signals found with long GRBs in O3 run; luminosity distance limits set assuming binary merger powering.