A neural network is trained to predict probabilities for lower mass gap components and neutron star involvement in gravitational-wave candidates, with reported mean errors of 9% and 6% on O4a events.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
No coincident GW signals found with long GRBs in O3 run; luminosity distance limits set assuming binary merger powering.
citing papers explorer
-
Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap
A neural network is trained to predict probabilities for lower mass gap components and neutron star involvement in gravitational-wave candidates, with reported mean errors of 9% and 6% on O4a events.
-
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.