arXiv subjects

Patrick Schillings

Publications and source records attributed to Patrick Schillings.

5 recordsLinked to original sources

arXiv2026

Effects of near-surface sedimentary structure on Newtonian noise for the Einstein Telescope: a 2-D numerical study

Near-surface low-velocity sediments can strongly modify seismic wavefields and therefore affect estimates of Newtonian noise at underground gravitational-wave observatories. We investigate these effects using 2-D viscoelastic simulations of a sediment layer overlying hard-rock basement. Controlled experiments examine the influence of sediment-basement interface geometry, sediment shear-wave velocity, attenuation, and test-mass position. Relative to a homogeneous model, the sediment layer produces frequency-dependent changes through wave trapping, interference, and attenuation. A constant-thickness layer generates a more laterally coherent wavefield and a sharper spectral enhancement than a basin-shaped interface, whereas lateral thickness variations broaden and shift the response. Sediment shear-wave velocity primarily controls the frequencies of the spectral features, while attenuation mainly controls their amplitudes. Newtonian noise is most sensitive to sediment structure for test masses located within or several hundred meters below the sediment layer. The sensitivity to lateral variations in sediment thickness decreases with burial depth and is weak at 2 km depth in the present model. These results demonstrate that near-surface sedimentary structure should be represented explicitly when assessing site-dependent Newtonian noise, particularly for test masses located between 200 m and 300 m depths.

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arXiv2026

A numerical framework for Newtonian-noise estimation at the Einstein Telescope: 2-D simulations beyond the plane-wave approximation

The Einstein Telescope (ET) is a third-generation underground gravitational-wave observatory designed to extend the detection sensitivity down to a few Hertz. Newtonian noise is expected to limit the low-frequency sensitivity of ET, particularly in the 3-15 Hz band. Most existing estimates rely on analytical or semi-analytical models assuming homogeneous or layered media, neglecting geological heterogeneity and complex wave interactions. In this work, we present a numerical framework for Newtonian-noise estimation based on spectral-element simulations of a seismic wave field. As a proof of concept, we first benchmark the numerical results against analytical plane-wave predictions in a two-dimensional homogeneous medium with a single surface source, demonstrating excellent agreement for both bulk and cavern contributions. We then extend the model to an array of 30 stochastic surface sources to approximate stationary ambient seismic excitation. The P-wave fraction inferred from the simulated wave field is, in this simple homogeneous case, significantly lower than commonly assumed, indicating enhanced prospects for Newtonian-noise mitigation. The framework is readily applicable to three-dimensional simulations and to integration of detailed local seismic models and topography, offering strong potential for site-specific Newtonian-noise estimation.

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arXiv2026

Optimization and robustness of cost-efficient seismic arrays for Newtonian noise cancellation at the Einstein Telescope

Newtonian noise is expected to be the dominating noise source for low frequencies at the Einstein Telescope. It originates from seismic waves that cause density fluctuations in the rock around the interferometer. The mitigation strategy for Newtonian noise relies on an array of seismometers, placed at depth in boreholes, which provides measurements of the seismic wave field. We optimize the positions of the individual seismometers for the mitigation capabilities of the array for a full corner of the Einstein Telescope. We find that the mitigation capabilities of arrays with multiple seismometers in each borehole match the capabilities of only somewhat smaller arrays but with only one seismometer per borehole. Mitigation is further improved by extending the array with seismometers in the interferometer tunnels. Such configurations may hence provide a cost-effective way towards realizing an efficient seismic array. In each case, we quantify the broadband mitigation performance in the range from 1 to 10 Hz for arrays that are optimized for a frequency of 10 Hz, as well as the robustness of the arrays with respect to variations from their optimized positions. We find that larger arrays with several seismometers per borehole and additional seismometers in the tunnels provide promising broadband performance above 3 to 4 Hz and that such arrays are particularly stable against variations in the seismometer positions with mitigation factors $>6$ for an array of 20 boreholes with 3 seismometers each and $>15$ for a large array of 50 boreholes with 10 seismometers.

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arXiv2026

NNNN: Neural Networks for Newtonian Noise Mitigation at the Einstein Telescope

The gravitational effects of seismic waves, so-called Newtonian noise, will likely limit the low-frequency sensitivity of future ground-based gravitational wave detectors, such as the Einstein Telescope. It has been proposed to mitigate this noise source by predicting it from measurements of the surrounding seismic displacement field using an array of seismometers. In this paper, we investigate the Newtonian noise prediction abilities of neural networks based on synthetic data from such seismometer arrays and compare the results with the Wiener filter as benchmark. We developed a simulation that generates density fluctuations of random plane waves and Gaussian wave packets, and that calculates the resulting Newtonian noise and displacement field. We investigate the performance on approximately stationary wave fields and single dominating long- and short-term events. For the first case, we observe comparable performance of neural networks and the Wiener filter with the networks performing slightly better. For the second case, however, we find that convolutional neural networks and graph neural networks can outperform the Wiener filter by factors of 15-80, depending on the frequency and the array configuration, and that they can reduce the corresponding Newtonian noise amplitude spectral density by factors of 10-30.

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arXiv2025

Fighting Newtonian Noise with Gradient-Based Optimization at the Einstein Telescope

Newtonian noise in gravitational wave detectors originates from density fluctuations in the adjacency of the interferometer mirrors. At the Einstein Telescope, this noise source is expected to be dominant for low frequencies. Its impact is proposed to be reduced with the help of an array of seismometers that will be placed around the interferometer endpoints. We reformulate and implement the problem of finding the optimal seismometer positions in a differentiable way. We then explore the use of first-order gradient-based optimization for the design of the seismometer array for 1 Hz and 10 Hz and compare its performance and computational cost to two metaheuristic algorithms. For 1 Hz, we introduce a constraint term to prevent unphysical optimization results in the gradient-based method. In general, we find that it is an efficient strategy to initialize the gradient-based optimizer with a fast metaheuristic algorithm. For a small number of seismometers, this strategy results in approximately the same noise reduction as with the metaheuristics. For larger numbers of seismometers, gradient-based optimization outperforms the two metaheuristics by a factor of 2.25 for the faster of the two and a factor of 1.4 for the other one, which is significantly outperformed by gradient-based optimization in terms of computational efficiency.

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