LSTM and CNN application for core-collapse supernova search in gravitational wave real data

$Context.$ Core-collapse supernovae (CCSNe) are expected to emit gravitational wave signals that could be detected by current and future generation interferometers within the Milky Way and nearby galaxies. The stochastic nature of the signal arising from CCSNe requires alternative detection methods to matched filtering. $Aims.$ We aim to show the potential of machine learning (ML) for multi-label classification of different CCSNe simulated signals and noise transients using real data. We compared the performance of 1D and 2D convolutional neural networks (CNNs) on single and multiple detector data. For the first time, we tested multi-label classification also with long short-term memory (LSTM) networks. $Methods.$ We applied a search and classification procedure for CCSNe signals, using an event trigger generator, the Wavelet Detection Filter (WDF), coupled with ML. We used time series and time-frequency representations of the data as inputs to the ML models. To compute classification accuracies, we simultaneously injected, at detectable distance of 1\,kpc, CCSN waveforms, obtained from recent hydrodynamical simulations of neutrino-driven core-collapse, onto interferometer noise from the O2 LIGO and Virgo science run. $Results.$ We compared the performance of the three models on single detector data. We then merged the output of the models for single detector classification of noise and astrophysical transients, obtaining overall accuracies for LIGO ($\sim99\%$) and ($\sim80\%$) for Virgo. We extended our analysis to the multi-detector case using triggers coincident among the three ITFs and achieved an accuracy of $\sim98\%$.

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

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.