Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification

TL;DR

This paper develops a deep learning approach combining CNNs and wavelet filters to detect and classify gravitational waves from core-collapse supernovae, achieving over 95% accuracy and robustness against noise artifacts.

Contribution

It introduces a novel CNN-based classification method for CCSN gravitational waves, incorporating noise transients and waveform model differentiation, tested with realistic detector noise.

Findings

Over 95% classification accuracy for single detector data

Robustness against detector noise transients demonstrated

CNN distinguishes between different CCSN waveform models

Abstract

We describe a search and classification procedure for gravitational waves emitted by core-collapse supernova (CCSN) explosions, using a convolutional neural network (CNN) combined with an event trigger generator known as Wavelet Detection Filter (WDF). We employ both a 1-D CNN search using time series gravitational-wave data as input, and a 2-D CNN search with time-frequency representation of the data as input. To test the accuracies of our 1-D and 2-D CNN classification, we add CCSN waveforms from the most recent hydrodynamical simulations of neutrino-driven core-collapse to simulated Gaussian colored noise with the Virgo interferometer and the planned Einstein Telescope sensitivity curve. We find classification accuracies, for a single detector, of over 95% for both 1-D and 2-D CNN pipelines. For the first time in machine learning CCSN studies, we add short duration detector noise transients to our data to test the robustness of our method against false alarms created by detector noise artifacts. Further to this, we show that the CNN can distinguish between different types of CCSN waveform models.