20–25 Aug 2023
Universität Hamburg
Europe/Berlin timezone

Gravity-Gradient Noise Mitigation via Deep Learning for the Einstein Telescope

Not scheduled
20m
Mensa Blattwerk (Universität Hamburg)

Mensa Blattwerk

Universität Hamburg

Von-Melle-Park 5
Poster Astroparticle Physics and Gravitational Waves Poster session

Speaker

David Bertram (RWTH Aachen University)

Description

As the first gravitational wave observatory of the third generation, the future Einstein Telescope (ET) aims to improve current sensitivities by at least one order of magnitude over the whole detection band. Specifically, in the low-frequency band below 10 Hz, gravity-gradient noise caused by seismic perturbations is anticipated to be the limiting noise contribution. Therefore, the underground construction and additional mitigation will be critical for ET to achieve design sensitivity. The associated challenge is a precise reconstruction of gravity-gradient noise based on the activity recorded by an array of auxiliary seismic sensors. On the poster, we present a first proof-of-concept for a model-independent approach based on a stochastic seismic simulation and analytical gravity-gradient noise model. In addition, cancellation performance and sensor noise robustness tests for a spatiotemporal ResNet architecture are discussed, along with the potential for sensor array optimization.

Collaboration / Activity Einstein Telescope

Primary author

David Bertram (RWTH Aachen University)

Co-authors

Achim Stahl (RWTH Aachen University) Markus Bachlechner (RWTH Aachen University) Oliver Pooth (RWTH Aachen)

Presentation materials