GlitchFlow: generative neural networks for de-noising gravitaional waves data in present and future detectors.

About

Third-generation (3G) gravitational waves (GW) detectors such as Einstein Telescope and Cosmic Explorer are expected to **increase annual GW detections** by a factor of 1000, enabling the detection of every stellar-mass black hole merger in the Universe. The growing volume and complexity of data generated by these observatories necessitate the application of data science techniques, particularly **deep learning**, to facilitate discoveries. One of the main challenges that 3G detectors will face is the reduction of **noise**. Specifically, the sensitivity will be most effected by the presence of transient noise in the form of **glitches** appearing in the detector data at much higeher rates than for previous interferometers. For this reason noise reduction and subtraction is one of the most important activities in GW research.

We present **GlitchFlow**, a novel Machine Learning pipeline for vetoing and de-noising on GW interferometer data. GlitchFlow uses a **Generative Neural Network** with a complex U-Net architecture to map carefully selected auxiliary channels (sensitive to the noise but not to GW signals) into the main channel of the interferometer. The generated output can then be subtracted from the interferometer main channel data. We achieve a de-noising accuracy **above 90% for glitches of SNR 6 or higher** on data from the Advanced Virgo detector. The input and ouput data consist of 2D spectrograms computed using the newly developed **Q-Transform Amplitude Modulation** (QTAM) package, which encodes all the physical information of the signals into amplitude and phase maps. This innovation makes it possible **invert the spectrograms and obtain the cleaned data as a 1D time series**, opening the path for hybrid 1D-2D analysis workflows and providing an output which can be directly fed into the standard **Parameter Estimation pipelines.**

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