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

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