Göttingen 2025 – scientific programme
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T: Fachverband Teilchenphysik
T 33: Data, AI, Computing, Electronics III (ML in Jet Tagging, Misc.)
T 33.6: Talk
Tuesday, April 1, 2025, 17:30–17:45, VG 2.101
Gravity Gradient Noise Mitigation using Deep Learning at the Einstein Telescope — Markus Bachlechner1, David Bertram1, Johannes Erdmann2, •Jan Kelleter2, Patrick Schillings2, and Achim Stahl1 — 1III. Physikalisches Institut B, RWTH Aachen — 2III. Physikalisches Institut A, RWTH Aachen
The Einstein Telescope is a proposed gravitational wave detector of the third generation. It aims to improve sensitivity by at least an order of magnitude compared to current detectors. The dominant noise source in the region of 1 to 10 Hz is expected to be gravity gradient noise (GGN) from seismic activity in the surrounding rock. In order to reach the desired sensitivity, GGN must be actively mitigated. Seismometers will be installed in boreholes around the mirrors to measure the seismic activity. The current gold standard to predict the mirror response from seismometer measurements is the application of linear filters. In this talk, we present an approach to using neural networks in order to predict the mirror response to GGN from simulated seismometer measurements.
Keywords: Gravity Gradient Noise; Einstein Telescope; Seismometer; Deep Learning; Gravitational Wave