Design of Seismic Newtonian Noise Cancellation Systems by Neural Network Enhanced Simulations

Are you passionate about cutting-edge research in simulations for seismic wave propagation in gravitational wave detectors? This project focuses on developing advanced Newtonian Noise Cancellation (NNC) systems for gravitational wave detectors designed to observe spacetime variations from cosmic events like black hole collisions. Newtonian noise, arising from mass density changes caused by seismic waves, poses significant challenges to these detectors, even those planned to be situated in a quiet underground, like the Einstein Telescope. Our project aims to enhance NNC systems by integrating advanced neural network-enhanced simulations and modern fiber optic strain sensors. We will investigate the interaction between complex geological features and seismic waves and how these interactions affect noise levels and detector sensitivity. These investigations involve modeling seismic wave propagation in heterogeneous environments and examining the coupling of seismic waves with fiber optic cables used as strain sensors on a very small scale, both in simulations and experimental data analysis. This research will advance sensor networks for their use in coherent noise-cancellation systems.
The project combines neural network-enhanced numerical simulations with cutting-edge technologies such as distributed acoustic sensing (DAS). It involves collaboration with leading experts in seismology, geophysics, and computational and data science. Our interdisciplinary approach will will not only advance the field of gravitational wave detection but will also contribute to geophysics by improving our understanding of seismic wave coupling and sensor calibration.
By developing robust, scalable solutions for future gravitational wave observatories, this project has the potential to significantly push the boundaries of scientific discovery and make a meaningful impact on the scientific community across diverse research fields.