Probabilistic kilonova prediction from gravitational wave inferred binary neutron star parameters

Abstract

Kilonovae provide a key electromagnetic window into binary neutron star mergers, revealing the properties of the merging system while probing r-process nucleosynthesis of the Universe. However, only a few kilonova candidates have been detected to date, with AT2017gfo being the only one associated with a gravitational wave event, GW170817. In this work, we present Genova, a probabilistic framework for predicting kilonova spectra and light curves directly from gravitational wave posterior samples of binary neutron star mergers. This method uses a conditional normalising flow to learn the distribution of rest-frame spectra conditioned on the source-frame component masses, tidal deformabilities, viewing angle and time since merger. Other kilonova model parameters, such as ejecta opacities, are marginalised over during training, so that their effects are propagated into the predicted spectra as predictive uncertainty. In the self-consistency test, the flow model reproduces the median light curves with residuals typically below $\sim 0.1$ mag, and the ratio of the predicted central 68% interval widths remains predominantly between $0.8$ and $1.4$ over $\sim 0.4$