Adaptation of a Convolutional Neural Network–based Pipeline to Detect Short Gravitational Wave Bursts
2024
Matteo Pracchia
We present a machine learning based pipeline to analyze unmodeled gravitational wave (GW) transients of less than 10 s. The convolutional neural network (CNN) is based on a U-NET architecture and takes as input data from GW interferometers represented as time-frequency maps, returning a spectrogram without the background noise. The CNN has been trained on simulated data, using a generated Gaussian background noise and injecting GW signals from core-collapse supernovae (CCSNe) simulations. The pipeline is able to successfully denoise spectrograms and recognize as signals also CCSNe waveforms for which it has not been trained on.
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