May 6 – 10, 2024
Europe/Rome timezone

Deep Learning Based Real-Time Noise Mitigation

May 7, 2024, 5:30 PM
1h 30m


Poster Instrument Science Board (ISB) Posters


Markus Bachlechner (RWTH Aachen University) Tim Kuhlbusch (RWTH Aachen University)


Mitigation techniques for Newtonian noise are essential due to the increasing sensitivity of future earth-based gravitational wave detectors. We are exploring deep learning as a model-independent technique to predict seismic-induced variations of the interferometer strain. Compared to conventional Wiener filters, convolutional neural networks can learn to distinguish a multiplicity of patterns and adapt to variations in the signal-to-noise ratio. Evaluating these networks on Field Programmable Gate Arrays (FPGAs) enables real-time prediction with high throughput and stable timing. We present a toolchain for optimizing the architecture of a quantized neural network to utilize the FPGA resources efficiently. In our lab setup, the network has outperformed a Wiener filter in canceling mechanically coupled vibrations in a small interferometer.

Primary authors

Markus Bachlechner (RWTH Aachen University) Tim Kuhlbusch (RWTH Aachen University) Achim Stahl (RWTH Aachen University) Dr Jochen Steinmann (RWTH Aachen University)

Presentation materials