Comparing inclination dependent analyses of kilonova transients

TL;DR

This paper compares different models of kilonova ejecta and radiative processes to assess their impact on inclination angle inference for AT2017gfo, highlighting the importance of accounting for model uncertainties in Bayesian analyses.

Contribution

It systematically evaluates how assumptions about ejecta and reprocessing affect parameter inference, especially inclination angles, in kilonova modeling.

Findings

Inclination angle posteriors vary with different ejecta geometries.

Including photon reprocessing improves model fits to AT2017gfo.

Large uncertainties (~1 mag) should be included to account for model systematics.

Abstract

The detection of AT2017gfo proved that binary neutron star mergers are progenitors of kilonovae. Using a combination of numerical-relativity and radiative-transfer simulations, the community has developed sophisticated models for these transients for a wide portion of the expected parameter space. Using these simulations and surrogate models made from them, it has been possible to perform Bayesian inference of the observed signals to infer properties of the ejected matter. It has been pointed out that combining inclination constraints derived from the kilonova with gravitational-wave measurements increases the accuracy with which binary parameters can be measured and allows a more accurate inference of the Hubble Constant. In order to not introduce biases, constraints on the inclination angle for AT2017gfo should be insensitive to the employed models. In this work, we compare different assumptions about the ejecta and radiative reprocesses used by the community and we investigate their impact on the parameter inference. While most inferred parameters agree, we find disagreement between posteriors for the inclination angle for different geometries that have been used in the literature. According to our study, the inclusion of reprocessing of the photons between different ejecta types improves the modeling fits to AT2017gfo and in some cases affects the inferred constraints. Our study motivates the inclusion of large $\sim$ 1 mag uncertainties in the kilonova models employed for Bayesian analysis to capture yet unknown systematics, especially when inferring inclination angles, although smaller uncertainties seem appropriate to capture model systematics for other parameters. We also use this method to impose soft constraints on the ejecta geometry of the kilonova AT2017gfo.