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The direct detection of gravitational waves by LIGO has heralded a new erafor astronomy and physics. Typically the gravitational waves observed by LIGOare dominated by noise. In this work we use Deep Convolutional Neural Networks(specifically U-Nets) to filter a clean signal from noisy data. We present tworealizations of U-Net filters, the Noise2Clean U-Net filter which is trainedusing noisy and clean realizations of the same signal, as well as Noise2NoiseU-Net which is trained on two separate noisy realization of the same signal. Wefind that the U-Nets successfully filter signal from noise. We also benchmarkthe performance of U-Nets by using them to detect the binary presence orabsence of gravitational wave signals in data.

Amplifying the Chirp: Using Deep Learning (U-Nets) to filter signal from noise in LIGO data