Nuclear physics constraints from binary neutron star mergers in the Einstein Telescope era

TL;DR

This paper evaluates how future gravitational-wave detectors like the Einstein Telescope can constrain the dense matter equation of state by analyzing binary neutron star mergers, employing advanced statistical methods and simulations.

Contribution

It provides a detailed analysis of the nuclear physics constraints achievable with ET, comparing detector configurations and using Bayesian methods to infer the neutron star EoS.

Findings

Over 500 high-SNR events can precisely determine the neutron star EoS.

Fisher information formalism is validated against full parameter estimation.

Different detector configurations impact the accuracy of EoS constraints.

Abstract

The next generation of ground-based gravitational-wave detectors, Einstein Telescope (ET) and Cosmic Explorer (CE), present a unique opportunity to put constraints on dense matter, among many other groundbreaking scientific goals. In a recent study the science case of ET was further strengthened, studying in particular the performances of different detector designs. In this paper we present a more detailed study of the nuclear physics section of that work. In particular, focusing on two different detector configurations (the single-site triangular-shaped design and a design consisting of two widely separated "L-shaped" interferometers), we study the detection prospects of binary neutron star (BNS) mergers, and how they can reshape our understanding of the underlying equation of state (EoS) of dense matter. We employ several state-of-the-art EoS models and state-of-the-art synthetic BNS merger catalogs, and we make use of the Fisher information formalism (FIM) to quantify statistical errors on the astrophysical parameters describing individual BNS events. To check the reliability of the FIM method, we further perform a full parameter estimation for a few simulated events. Based on the uncertainties on the tidal deformabilities associated to these events, we outline a mechanism to extract the underlying injected EoS using a recently developed meta-modelling approach within a Bayesian framework. Our results suggest that with $\gtrsim 500$ events with signal-to-noise ratio greater than $12$, we will be able to pin down very precisely the underlying EoS governing the neutron star matter.