Speaker
Description
Seismic Newtonian noise (NN) is expected to limit the low-frequency sensitivity of the Einstein Telescope (ET), requiring advanced mitigation strategies. Current approaches rely on seismometer arrays and stochastic models, but are constrained by limited spatial sampling and difficulties in separating compressional (P) and shear (S) wave contributions.
We investigate the potential of fusion sensor arrays combining multiple sensor types. In particular, we consider distributed acoustic sensing (DAS), which enables dense strain measurements along optical fibres, and advanced seismic stations measuring both translational and rotational ground motion. Such instrumentation is already partly realized in systems like LIGO and is foreseen for ET infrastructure.
Using a fusion sensor Wiener filtering framework, we evaluate NN cancellation performance for selected deployment scenarios, focusing on improved P/S-wave disentanglement. Our results indicate that combining complementary sensors significantly enhances seismic field reconstruction and can therefore support NN mitigation, supporting the low-frequency sensitivity goals of the ET.