Classification of the core-collapse supernova explosion mechanism with learned dictionaries

Abstract

Core-collapse supernovae (CCSN) 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 SupernovaModel Evidence Extractor (SMEE; Logue et al. (2012)), 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 catalogs built from numerical simulations of neutrino-driven (Mur) and magneto-rotational (Dim) CCSN explosions and (b) from a third 'mock' catalog of simulated sine-Gaussian (SG) waveforms. Those signals are injected into coloured Gaussian noise to simulate the background noise of Advanced LIGO in its broadband 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% 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.

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Cited by in corpus (8)

• The Science of the Einstein Telescope
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Exploring Supernova Gravitational Waves with Machine Learning
• Multi-messenger observations of core-collapse supernovae: Exploiting the standing accretion shock instability
• Probing nuclear physics with supernova gravitational waves and machine learning
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification
• Parameter estimation of protoneutron stars from gravitational wave signals using the Hilbert-Huang transform