Modelling Populations of Kilonovae
arXiv:2205.12286 · doi:10.1093/mnras/stad257
Abstract
The 2017 detection of a kilonova coincident with gravitational-wave emission has identified neutron star mergers as the major source of the heaviest elements, and dramatically constrained alternative theories of gravity. Observing a population of such sources has the potential to transform cosmology, nuclear physics, and astrophysics. However, with only one confident multi-messenger detection currently available, modelling the diversity of signals expected from such a population requires improved theoretical understanding. In particular, models which are quick to evaluate, and are calibrated with more detailed multi-physics simulations, are needed to design observational strategies for kilonovae detection, and to obtain rapid-response interpretations of new observations. We use grey-opacity models to construct populations of kilonovae, spanning ejecta parameters predicted by numerical simulations. Our modelling focuses on wavelengths relevant for upcoming optical surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST). In these simulations, we implement heating rates that are based on nuclear reaction network calculations. We create a Gaussian-process emulator for kilonova grey opacities, calibrated with detailed radiative transfer simulations. Using recent fits to numerical relativity simulations, we predict how the ejecta parameters from BNS mergers shape the population of kilonovae, accounting for the viewing-angle dependence. Our simulated population of binary neutron star (BNS) mergers produce peak i-band absolute magnitudes . A comparison with detailed radiative transfer calculations indicates that further improvements are needed to accurately reproduce spectral shapes over the full light curve evolution.
arXiv comment14 pages, 12 figures, 2 tables, matches version accepted to MNRAS. Fixes a software bug from previous versions which incorrectly determined the kilonovae ejecta velocities, v4: added link to public code package https://github.com/cnsetzer/kilopop
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