Technical and environmental noise in ground-based laser interferometers designed for gravitational-wave observations like Advanced LIGO, Advanced Virgo and KAGRA, can manifest as narrow ($<1\mathrm{Hz}$) or broadband (${10}^{\prime }\mathrm{s}$ or even ${100}^{\prime }\mathrm{s}$ of Hz) spectral lines and features in the instruments’ strain amplitude spectral density. When the sources of this noise cannot be identified or removed, in cases where there are witness sensors sensitive to this noise source, denoising of the gravitational-wave strain channel can be performed in software, enabling recovery of instrument sensitivity over the affected frequency bands. This noise hunting and removal process can be particularly challenging due to the wealth of auxiliary channels monitoring the interferometry and the environment and the nonlinear couplings that may be present. In this work, we present a comprehensive analysis approach and corresponding cyberinfrastructure to promptly identify and remove noise in software using machine learning techniques. The approach builds on earlier work (referred to as deepclean) in using machine learning methods for linear and nonlinear regression of noise. We demonstrate how this procedure can be operated and optimized in a tandem fashion close to online data taking; it starts off with a coherence monitoring analysis that first singles out and prioritizes witness channels that can then be used by deepclean. The resulting denoised strain by deepclean reflects a 1.4% improvement in the binary neutron star range, which can translate into a 4.3% increase in the sensitive astrophysical volume. This cyberinfrastructure that we refer to as coherence deepclean, or CDC, is a significant step toward autonomous operations of noise subtraction for ground-based interferometers.

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• Gravitational wave detection
• Gravitational waves
• Gravitational wave detectors