[Paper Review] Gravitational Waves from Neutrino-Driven Core Collapse Supernovae: Predictions, Detection, and Parameter Estimation

Anthony Mezzacappa, M. Zanolin|arXiv (Cornell University)|Jan 22, 2024

Neutrino Physics Research4 citations

TL;DR

This paper presents a comprehensive review of gravitational wave (GW) emission from neutrino-driven core-collapse supernovae (CCSNe), integrating multi-physics simulations, GW detection strategies, and parameter estimation techniques. It demonstrates that GW signals from key supernova phases—core bounce, proto-neutron star convection, and SASI—can be detected by ground-based interferometers, with advanced data analysis enabling accurate inference of progenitor and central engine properties.

ABSTRACT

Three-dimensional modeling has reached a level of maturity to provide detailed predictions of the gravitational wave emission in neutrino-driven core collapse supernovae. We review the status of these modeling efforts, current predictions for core collapse supernova gravitational wave emission, and the status of algorithms for the detection of core collapse supernova gravitational waves and the estimation of physical parameters associated with these events, which we hope to use to cull information about the central engine.

Motivation & Objective

• To synthesize current understanding of gravitational wave emission from neutrino-driven core-collapse supernovae using multi-physics simulations.
• To develop and evaluate detection strategies for GWs from Galactic or near-extra-Galactic CCSNe using ground-based interferometers.
• To enable precise parameter estimation of progenitor and central engine properties from GW signals, leveraging multimessenger data.
• To bridge the gap between high-fidelity supernova simulations and GW data analysis by identifying observable GW features and optimizing detection windows.
• To support the preparation for the first multimessenger detection of a CCSN by integrating GW, neutrino, and electromagnetic data.

Proposed method

• Utilizes general relativistic, multi-dimensional hydrodynamics and neutrino transport simulations to model CCSN dynamics and GW emission.
• Applies a formalism for calculating GW strain and spectrograms from asymmetric mass motions and neutrino-driven convection in the proto-neutron star region.
• Employs matched filtering and template-based detection strategies adapted for CCSNe, accounting for long-duration, low-amplitude GW signals.
• Integrates physics-based and empirical light curve models to estimate the time of shock breakout (SBO) and define optimal search windows (OSWs) for GW detection.
• Uses Bayesian inference and posterior sampling to estimate progenitor and central engine parameters from simulated GW data.
• Evaluates detection efficiency and sensitivity using duty cycle analysis and correlation with electromagnetic and neutrino triggers.

Figure 1: Sources of CCSN GW emission (taken from Andresen et al. ( 2017 ) ), beginning with sustained Ledoux convection in Region A 1 deep within the PNS, moving outward to convective overshoot in Region A 2 , then on to neutrino-driven convection in the gain layer, Region C, which generates GWs pe

Experimental results

Research questions

• RQ1 What are the characteristic gravitational wave signatures from core bounce, proto-neutron star convection, and the SASI in core-collapse supernovae?
• RQ2 How can detection strategies be optimized to maximize sensitivity to low-amplitude, long-duration CCSN GW signals in the presence of instrumental noise?
• RQ3 What role do electromagnetic and neutrino triggers play in defining effective observation windows for targeted GW searches?
• RQ4 How accurately can progenitor and central engine parameters be estimated from GW data using Bayesian parameter estimation?
• RQ5 What are the key challenges in aligning high-fidelity supernova simulations with detectable GW features for future multimessenger astronomy?

Key findings

• Gravitational wave emission from core bounce, proto-neutron star convection, and the SASI are detectable by ground-based interferometers, with spectral features lying within the most sensitive frequency bands.
• A quartic polynomial fit to light curves provides more accurate shock breakout time estimates than linear or quadratic fits, especially when early data are missing.
• Optimal search windows (OSWs) with 90–100% probability of containing the GW signal significantly improve detection chances compared to longer, less certain OSWs.
• Physics-based modeling and matched filtering with template banks yield posterior distributions for GW arrival times, enhancing targeted search efficiency.
• The duty cycle for coincident science-quality data across interferometers varies from 20% to nearly 90%, impacting the sensitivity of all-sky and targeted searches.
• Parameter estimation from GW data reveals biases when using suboptimal models, but advanced fitting techniques reduce these errors, improving accuracy in inferring core bounce time and progenitor properties.

Figure 2: CCSN GW strains (at the source) for both polarizations for a progenitor of mass 15 M ⊙ , from Mezzacappa et al. ( 2023 ) .