A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae

Abstract

While gravitational waves have been detected from mergers of binary black holes and binary neutron stars, signals from core collapse supernovae, the most energetic explosions in the modern Universe, have not been detected yet. Here we present a new method to analyse the data of the LIGO, Virgo and KAGRA network to enhance the detection efficiency of this category of signals. The method takes advantage of a peculiarity of the gravitational wave signal emitted in the core collapse supernova and it is based on a classification procedure of the time-frequency images of the network data performed by a convolutional neural network trained to perform the task to recognize the signal. We validate the method using phenomenological waveforms injected in Gaussian noise whose spectral properties are those of the LIGO and Virgo advanced detectors and we conclude that this method can identify the signal better than the present algorithm devoted to select gravitational wave transient signal.

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Cited by in corpus (6)

• Deep learning for multimessenger core-collapse supernova detection
• Dependence of outer boundary condition on protoneutron star asteroseismology with gravitational-wave signatures
• The final core collapse of pulsational pair instability supernovae
• Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise
• Gravitational-wave Signals From Three-dimensional Supernova Simulations With Different Neutrino-Transport Methods
• Computational Techniques for Parameter Estimation of Gravitational Wave Signals