Classification of core-collapse supernova explosions with learned dictionaries

Core-collapse supernovae (CCSNs) are a prime source of gravitational waves. Estimations of their typical frequencies make them perfect targets for the current network of advanced, ground-based detectors. A successful detection could potentially reveal the underlying explosion mechanism through the analysis of the waveform. This has been illustrated using the Supernova Model Evidence Extractor (SMEE), an algorithm based on principal component analysis and Bayesian model selection. Here, we present a complementary approach to SMEE based on (supervised) dictionary-learning and show that it is able to reconstruct and classify CCSN signals according to their morphology. Our waveform signals are obtained from (a) two publicly available catalogues built from numerical simulations of neutrino-driven (Mur) and magneto-rotational (Dim) CCSN explosions and (b) from a third 'mock' catalogue of simulated sine-Gaussian (SG) waveforms. All of these signals are injected into coloured Gaussian noise to simulate the background noise of Advanced LIGO in its broad-band configuration and scaled to a freely specifiable signal-to-noise ratio (SNR). We show that our approach correctly classifies signals from all three dictionaries. In particular, for SNR = 15-20, we obtain perfect matches for both Dim and SG signals and about 85 per cent true classifications for Mur signals. These results are comparable to those reported by SMEE for the same CCSN signals when those are injected in only one LIGO detector. We discuss the main limitations of our approach as well as possible improvements.

Automated summary

This study presents a complementary approach to the Supernova Model Evidence Extractor (SMEE) based on dictionary learning for reconstructing and classifying core-collapse supernova (CCSN) signals according to their morphology. The results show that this approach can successfully classify different types of CCSN signals.