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.
as written by the authors arXiv:2605.00391
The physics governing the boundary between the most massive neutron stars (NSs) and the least massive black holes (BHs) is currently uncertain, but could potentially be constrained with new observations. While NSs have been observed with masses up to $$\sim2~M_{\odot}$$, there is a dearth of electromagnetic observations of compact objects in the $$\sim2-5~M_{\odot}$$ range, known as the lower mass gap. Recent observations of gravitational-wave (GW) signals from binary mergers detected by the LIGO-Virgo-KAGRA (LVK) collaboration indicate that this gap is likely not empty. Rapidly distinguishing whether a candidate GW event has components in this purported mass gap can indicate the likelihood of a detectable electromagnetic counterpart, and thus inform decisions for follow-up observations. In this work we train a neural network model, GWSkyNet-MassGap, that simultaneously predicts the probability that a candidate merger has a component in the lower mass gap ($$P_{\mathrm{MassGap}}$$) and the probability that it involves a NS ($$P_{\mathrm{NS}}$$). We find that the model is able to infer information about the source chirp mass to predict $$P_{\mathrm{MassGap}}$$ and $$P_{\mathrm{NS}}$$, leading to correct predictions for high-mass mergers with $$\mathcal{M}_c\gtrsim15~M_{\odot}$$, but less accurate predictions for lower-mass systems which require knowledge of the binary mass ratio to break the mass degeneracy. For candidate events in the first part of LVK's fourth observing run (O4a), the model has a mean prediction error of 9% for $$P_{\mathrm{MassGap}}$$ and 6% for $$P_{\mathrm{NS}}$$. The model could be further developed to rapidly predict the source chirp mass for candidate events in future observing runs.
A sugar-cube-sized lump of neutron star matter would outweigh Mount Everest, making it the universe's densest known material outside a black hole.