A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae. (arXiv:1812.05363v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Astone_P/0/1/0/all/0/1">P. Astone</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cerda_Duran_P/0/1/0/all/0/1">P. Cerda-Duran</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Palma_I/0/1/0/all/0/1">I. Di Palma</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Drago_M/0/1/0/all/0/1">M. Drago</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Muciaccia_F/0/1/0/all/0/1">F. Muciaccia</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Palomba_C/0/1/0/all/0/1">C. Palomba</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ricci_F/0/1/0/all/0/1">F. Ricci</a>
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.
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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