Inferring neutron star merger ejecta morphologies with kilonovae

TL;DR

This paper evaluates the ability of upcoming observational strategies to determine neutron star merger ejecta morphologies using new kilonova simulations, highlighting the importance of mid-infrared observations with JWST.

Contribution

Introduces a new grid of kilonova simulations with four ejecta morphologies and assesses their effectiveness in inferring ejecta structures from observational data.

Findings

Late-time JWST MIR observations are crucial for morphology discrimination.

Vera Rubin Observatory alone is insufficient for ejecta morphology determination.

SuperNu and POSSIS models fit kilonova data similarly, with SuperNu slightly preferred.

Abstract

In this study we incorporate a new grid of kilonova simulations produced by the Monte Carlo radiative transfer code SuperNu in an inference pipeline for astrophysical transients, and evaluate their performance. These simulations contain four different two-component ejecta morphology classes. We analyze follow-up observational strategies by Vera Rubin Observatory in optical, and James Webb Space Telescope (JWST) in mid-infrared (MIR). Our analysis suggests that, within these strategies, it is possible to discriminate between different morphologies only when late-time JWST observations in MIR are available. We conclude that follow-ups by the new Vera Rubin Observatory alone are not sufficient to determine ejecta morphology. Additionally, we make comparisons between surrogate models based on radiative transfer simulation grids by SuperNu and POSSIS, by analyzing the historic kilonova AT2017gfo that accompanied the gravitational wave event GW170817. We show that both SuperNu and POSSIS models provide similar fits to photometric observations. Our results show a slight preference for SuperNu models, since the wind ejecta parameters recovered with these models are in better agreement with expectations from numerical simulations.