Machine learning for gravitational-wave detection: surrogate Wiener filtering for the prediction and optimized cancellation of Newtonian noise at Virgo

Abstract

The cancellation of noise from terrestrial gravity fluctuations, also known as Newtonian noise (NN), in gravitational-wave detectors is a formidable challenge. Gravity fluctuations result from density perturbations associated with environmental fields, e.g., seismic and acoustic fields, which are characterized by complex spatial correlations. Measurements of these fields necessarily provide incomplete information, and the question is how to make optimal use of available information for the design of a noise-cancellation system. In this paper, we present a machine-learning approach to calculate a surrogate model of a Wiener filter. The model is used to calculate optimal configurations of seismometer arrays for a varying number of sensors, which is the missing keystone for the design of NN cancellation systems. The optimization results indicate that efficient noise cancellation can be achieved even for complex seismic fields with relatively few seismometers provided that they are deployed in optimal configurations. In the form presented here, the optimization method can be applied to all current and future gravitational-wave detectors located at the surface and with minor modifications also to future underground detectors.

References in corpus (21)

• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• The Loss Surfaces of Multilayer Networks
• Seismic gravity-gradient noise in interferometric gravitational-wave detectors
• Metrics for next-generation gravitational-wave detectors
• Improving astrophysical parameter estimation via offline noise subtraction for Advanced LIGO
• Subtraction of Newtonian Noise Using Optimized Sensor Arrays
• Parameter estimation for binary black holes with networks of third generation gravitational-wave detectors
• Terrestrial Gravity Fluctuations
• Binary black hole spectroscopy
• Tumbleweeds and airborne gravitational noise sources for LIGO
• Impact of infrasound atmospheric noise on gravity detectors used for astrophysical and geophysical applications
• Optimization of seismometer arrays for the cancellation of Newtonian noise from seismic body waves
• Implications of dedicated seismometer measurements on Newtonian-noise cancellation for Advanced LIGO
• Towards a first design of a Newtonian-noise cancellation system for Advanced LIGO
• Wiener filtering with a seismic underground array at the Sanford Underground Research Facility
• Seismic array measurements at Virgo's West End Building for the configuration of a Newtonian-noise cancellation system
• Observation of a potential future sensitivity limitation from ground motion at LIGO Hanford
• Investigation of magnetic noise in Advanced Virgo
• Resonant Hawking radiation as an instability
• Effect of squeezing on parameter estimation of gravitational waves emitted by compact binary systems
• Matrix String Theory on pp-waves

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• Enabling multi-messenger astronomy with continuous gravitational waves: early warning and sky localization of binary neutron stars in Einstein Telescope
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