From Post Hoc Subtraction to Source Suppression: ML Noise Mitigation in Advanced LIGO

About

We present our work on machine learning for noise mitigation in Advanced LIGO that moves from software denoising of the strain channel to suppression of disturbances at their source within the detector control system. Using Coherence DeepClean, we perform coherence-based witness-channel selection followed by machine-learning regression to subtract linear and nonlinear noise couplings from the gravitational-wave readout after they enter the strain data. This software denoising approach has yielded measurable sensitivity gains, including a 4.3% improvement in astrophysical sensitive volume. We extend the same data-driven philosophy upstream to the seismic isolation system, where neural-network models predict residual platform motion induced by persistent microseismic activity in the 0.1–0.3 Hz band. In contrast to post hoc subtraction, this method targets the disturbance before it propagates through the instrument, enabling direct suppression of motion at the source. Our results suggest that, if integrated into the control system, the method could offer up to an order-of-magnitude reduction in residual motion compared to conventional linear filtering. Looking ahead, we are exploring reinforcement learning for increasingly autonomous control architectures. Together, our results outline a path toward autonomous machine-learning systems for improving detector stability, low-frequency sensitivity, and astrophysical reach.

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