Physicists debate the boundary between neutron stars and black holes. Electromagnetic observations find almost no objects with masses between 2 and 5 solar masses — this is the 'lower mass gap.' However, gravitational waves hint that it might not be empty. Quickly determining whether a merger contains such an object is important for predicting a light flash. The GWSkyNet-MassGap neural network predicts two probabilities: one for a component falling into the gap, and another for the presence of a neutron star. It uses chirp mass (a signal characteristic) and is accurate for heavy systems (>15 M⊙), but errs for light ones, where the mass ratio matters. On data from O4a, the errors were 9% and 6%. In the future, the model could rapidly determine chirp mass.
When black holes or neutron stars merge, they strum space, broadcasting gravitational waves—a chirp predicted by Einstein. Detectors like LIGO capture this cosmic ringtone, its pitch encoding the masses. Yet, there's a silent interval in the cosmic scale: objects from 2 to 5 Suns are almost never heard. This lower mass gap lies between the heaviest neutron stars and lightest black holes.
GWSkyNet-MassGap, a neural network, listens to the gravitational wave chirp and judges if a merger includes a neutron star or a gap object—like naming instruments in an orchestra. It's accurate for loud signals but can mishear the mass split in faint ones. If neutron stars collide, telescopes catch a kilonova forging gold; black hole mergers stay dark. Sifting gravitational wave catalogs, it spotlights rare candidates. The gap may hide surprises—perhaps quark-matter ghosts that are neither star nor hole.
🎯 A sugar-cube-sized lump of [tag:neutron_star]neutron star[/tag] matter would outweigh Mount Everest, making it the universe's densest known material outside a black hole.