Phenomenological gravitational waveforms for core-collapse supernovae

TL;DR

This paper introduces a fast, parametrized phenomenological waveform generator for core-collapse supernovae, enabling efficient data analysis and neural network training for gravitational wave detection.

Contribution

A novel low-cost waveform generator that accurately reproduces supernova gravitational waveforms, including polarization and oscillation modes, calibrated with numerical simulations.

Findings

Waveform generation time reduced to ~10 ms

Includes polarization and oscillation modes in waveforms

Calibrated with numerical simulation data

Abstract

Galactic core-collapse supernovae (CCSNe) are a target for current generation gravitational wave detectors with an expected rate of 1-3 per century. The development of data analysis methods used for their detection relies deeply on the availability of waveform templates. However, realistic numerical simulations producing such waveforms are computationally expensive (millions of CPU hours and $10^2-10^3$~GB of memory), and only a few tens of them are available nowadays in the literature. We have developed a novel parametrized phenomenological waveform generator for CCSNe, ccphen v4, that reproduces the morphology of numerical simulation waveforms with low computational cost ($\sim 10$~ms CPU time and a few MB of memory use). For the first time, the phenomenological waveforms include polarization and the effect of several oscillation modes in the proto-neutron star. This is sufficient to describe the case of non-rotating progenitor cores, representing the vast majority of possible events. The waveforms include a stochastic component and are calibrated using numerical simulation data. The code is publicly available. Their main application is the training of neural networks used in detection pipelines, but other applications in this context are also discussed.