Bayesian inference for gravitational waves from binary neutron star mergers in third-generation observatories

TL;DR

This paper extends Bayesian inference methods to efficiently analyze long-duration gravitational-wave signals from binary neutron star mergers in third-generation detectors, enabling rapid and detailed parameter estimation.

Contribution

It introduces reduced order models for 90-minute signals that incorporate key physics, significantly speeding up Bayesian inference for complex, overlapping signals in third-generation observatories.

Findings

Inference speed increased by a factor of ~13,000 with reduced order models.

Models include effects of tidal deformability, Earth's rotation, and spin precession.

Bayesian inference remains feasible for high SNR, long-duration signals in 3G detectors.

Abstract

Third-generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced order models for $\sim 90\,\mathrm{minute}$ long gravitational-wave signals, covering the observing band ($5-2048\,\mathrm{Hz}$), speeding up inference by a factor of $\sim 1.3\times 10^4$ compared to the calculation times without reduced order models. The reduced order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to the Earth's rotation, and spin-induced orbital precession. We show how reduced order modeling can accelerate inference on data containing multiple, overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories.