Bo-Xuan Ge

We investigate whether gravitational waveforms from massive boson-star mergers can be used to diagnose the underlying merger outcome. Using an existing numerical-relativity catalogue, we construct a branch-conditioned neural reconstruction model and infer the outcome by comparing the reconstruction quality of candidate waveform hypotheses. This makes the diagnosis waveform-based rather than a direct classification in the initial parameter space. We compare a supervised baseline model with a distilled student model and find that the merger outcome is encoded in the waveform morphology and can be recovered through branch-conditioned reconstruction. Our results provide a first step toward waveform-based classification of exotic compact-object mergers with multiple possible final states.