Silencing Newtonian noise using fusion sensor arrays

TL;DR

This paper explores the use of combined seismometer and DAS sensor arrays to improve Newtonian noise mitigation for gravitational-wave detectors, demonstrating cost-effective configurations that enhance noise cancellation performance.

Contribution

It introduces a novel fusion sensor array approach with extended Wiener filtering and analytic S-wave correlations for improved NN mitigation in gravitational-wave detectors.

Findings

Fusion arrays improve P/S-wave separation and NN cancellation.

Six seismometers plus fourteen strainmeters inside ET arms match borehole seismometer performance.

Cost-effective sensor configurations achieve 10% residual NN level.

Abstract

Newtonian noise (NN) from seismic density fluctuations is expected to limit the low-frequency sensitivity of third-generation gravitational-wave detectors, in particular the Einstein Telescope (ET). Current NN mitigation relies on seismometer arrays and Wiener filtering, while distributed acoustic sensing (DAS) offers a complementary, low-cost means of obtaining dense strain measurements. We investigate fusion sensor arrays composed of both displacement-measuring seismometers and strain-measuring DAS-type sensors. We extend the Wiener filter formalism to mixed sensor types and introduce analytic S-wave strain correlation coefficients. Using a hybrid differential evolution and covariance matrix adaptation scheme, we validate our approach against established seismometer-only results and analyze the geometry, robustness, and performance of optimized fusion arrays. Fusion arrays enhance P/S-wave disentanglement and achieve NN cancellation levels comparable to, and sometimes exceeding, those of seismometer-only arrays, particularly for small sensor numbers. When sensors are constrained to the ET infrastructure, we find that six seismometers complemented by fourteen strainmeters inside the ET arms can match the performance of twenty seismometers in boreholes, achieving a residual at the 10% level, and thereby offering a cost-efficient pathway toward ET-scale NN mitigation.