What to expect: kilonova light curve predictions via equation of state marginalization

TL;DR

This paper introduces new low-latency kilonova light curve and ejecta mass data products derived from gravitational-wave merger measurements, accounting for neutron star equation of state uncertainties, to improve multi-messenger follow-up observations.

Contribution

It presents a novel method to produce kilonova light curve predictions and ejecta mass estimates from merger data, marginalizing over neutron star equation of state uncertainties, for rapid multi-messenger analysis.

Findings

Generated kilonova light curve predictions using surrogate models trained on Monte Carlo radiative transfer simulations.

Proposed new data products including ejecta mass probability and light curve estimates for low-latency gravitational-wave alerts.

Integrated these data products into the IGWN alert infrastructure for future multi-messenger observations.

Abstract

Efficient multi-messenger observations of gravitational-wave candidates from compact binary coalescence candidate events rely on data products reported in low-latency by the International Gravitational-wave Network (IGWN). While data products such as $\texttt{HasNS}$, the probability of at least one neutron star, and $\texttt{HasRemnant}$, the probability of remnant matter forming after merger, exist, these are not direct observables for a potential kilonova. Here, we present new kilonova light curve and ejecta mass data products derived from merger quantities measured in low latency, by marginalizing over our uncertainty in our understanding of the neutron star equation of state and using measurements of the source properties of the merger, including masses and spins. Two additional types of data products are proposed. The first is the probability of a candidate event having mass ejecta ($m_{\mathrm{ej}}$) greater than $10^{-3} M_\odot$, which we denote as $\texttt{HasEjecta}$. The second are $m_{\mathrm{ej}}$ estimates and accompanying $\texttt{ugrizy}$ and $\texttt{HJK}$ kilonova light curves predictions produced from a surrogate model trained on a grid of kilonova light curves from $\texttt{POSSIS}$, a time-dependent, three-dimensional Monte Carlo radiative transfer code. We are developing these data products in the context of the IGWN low-latency alert infrastructure, and will be advocating for their use and release for future detections.