Non-linear noise regression in the Virgo detector

About

Since the first detection of gravitational waves from a binary black hole merger, hundreds of such events have been observed. However, many compact binary coalescence signals remain buried below the detector noise threshold and could be recovered through improved noise mitigation. Detector noise arises from multiple sources, including fundamental, technical, and environmental contributions (seismic activity, atmospheric fluctuations, thermal variations, and Newtonian noise). These sources can be stationary, non-stationary, or non-linear; the latter generate complex and unpredictable patterns that make signal recovery particularly challenging. This work presents the first demonstration of non-linear noise regression using deep learning techniques within Virgo. Using DeepClean, a convolutional neural network, both stationary and non-stationary noise contributions are modeled and subtracted from strain data, revealing previously unmodeled non-linear noise couplings and improving detector sensitivity. The method leads to measurable performance gains, including an increase of up to 1.3 Mpc (~2.5%) in the binary neutron star inspiral range and a ~1.7% average improvement in recovered signal-to-noise ratio for injected binary black hole signals. These results demonstrate the potential of machine learning-based noise mitigation for enhancing gravitational-wave detection and enabling low-latency transient searches.

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