Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap
This paper introduces GWSkyNet-MassGap, a neural network model trained to rapidly predict the probabilities of gravitational-wave candidates containing components in the lower mass gap or being neutron stars, achieving high accuracy for high-mass mergers while highlighting the need for mass ratio information to resolve degeneracies in lower-mass systems.