Speaker
Description
Accurate modeling of Newtonian noise (NN) is a critical requirement for the low-frequency sensitivity of next-generation gravitational-wave detectors such as the Einstein Telescope (ET). In this work, we investigate the consistency and convergence of different approaches used to compute gravitoelastic correlations induced by seismic fields.
In practical applications, arrays of seismic sensors are deployed to measure the ambient seismic field. These measurements serve as inputs to NN models, enabling the estimation of gravitoelastic correlations and, ultimately, the mitigation of Newtonian noise through subtraction techniques. This highlights the need for reliable and well-validated modeling frameworks.
We develop numerical models and closed-form analytical solutions and validate the numerical implementations through systematic comparison with analytical results. This framework enables the direct use of displacement correlations between sensors as model inputs, reflecting the practical scenario where such correlations are inferred from seismic measurements. The study considers both body waves (P and S) and Rayleigh waves, and explores different configurations of the test mass, including underground and above-ground setups.
Our results demonstrate the agreement between theoretical formulations and numerical implementations, and establish a foundation for robust NN prediction. This work contributes to the development of reliable modeling tools for the Einstein Telescope and supports future efforts in sensor array optimization and noise mitigation.