# Characterizing Earth gravity field fluctuations with the MIGA antenna   for future Gravitational Wave detectors

**Authors:** J. Junca, A. Bertoldi, D.O. Sabulsky, G. Lef\`evre, X. Zou, J.-B., Decitre, R. Geiger, A. Landragin, S. Gaffet, P. Bouyer, B. Canuel

arXiv: 1902.05337 · 2019-05-22

## TL;DR

This paper discusses how the MIGA atom interferometry antenna can measure and characterize gravity gradient noise caused by seismic and atmospheric fluctuations, which is crucial for future low-frequency gravitational wave detection.

## Contribution

It introduces the use of the MIGA experiment to model and analyze gravity gradient noise for low-frequency GW observatories, utilizing seismic and atmospheric data.

## Key findings

- MIGA can effectively characterize GGN using dedicated data analysis.
- Seismic and atmospheric data inform GGN impact on low-frequency GW detection.
- Modeling shows potential for GGN mitigation in future observatories.

## Abstract

Fluctuations of the earth's gravity field are a major noise source for ground-based experiments investigating general relativity phenomena such as Gravitational Waves (GWs). Mass density variations caused by local seismic or atmospheric perturbations determine spurious differential displacements of the free falling test masses, what is called Gravity Gradient Noise (GGN); it mimics GW effects. This GGN is expected to become dominant in the infrasound domain and must be tackled for the future realization of observatories exploring GWs at low frequency. GGN will be studied with the MIGA experiment, a demonstrator for low frequency GW detection based on atom interferometry - now in construction at the low noise underground laboratory LSBB in France. MIGA will provide precise measurements of local gravity, probed by a network of three free-falling atom test masses separated up to 150~m. We model the effect of GGN for MIGA and use seismic and atmospheric data recorded at LSBB to characterize their impact on the future measurements. We show that the antenna will be able to characterize GGN using dedicated data analysis methods.

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Source: https://tomesphere.com/paper/1902.05337