Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors

TL;DR

This study uses machine learning to estimate key parameters of core-collapse supernovae from gravitational wave signals, assessing current and future detector capabilities and the impact of physical uncertainties.

Contribution

It extends previous work by analyzing a broader set of models, uncertainties, and detector sensitivities, improving parameter estimation methods for supernova GW signals.

Findings

Uncertainty in bounce time does not significantly impact Fourier domain analysis.

Optimal orientations enable constraining rotation beyond 100 kpc with next-generation detectors.

Physical uncertainties like source inclination affect parameter estimation accuracy.

Abstract

Core-collapse supernovae (CCSNe) are powerful sources of gravitational waves (GWs). These signals propagate essentially unobstructed, providing a unique probe of the supernova central engine. In this work, we investigate parameter estimation from the bounce and early ring-down GW signal of rotating CCSNe using machine learning. We infer the peak frequency and peak amplitude of the signal as well as the rotation of the core. We extend previous studies in several directions. We consider a range of progenitor models and nuclear equations of state, and we assess the impact of key physical uncertainties, including bounce-time uncertainty and source inclination. We incorporate both current detector noise and the projected sensitivities of next-generation observatories. We find that uncertainty in the bounce time does not significantly affect parameter estimation when the analysis is performed in the Fourier domain. In contrast, orientations when the rotation axis is near the line of sight substantially degrade performance. For optimal orientations, next-generation detectors can constrain rotation out to distances exceeding 100 kpc.