Hybrid electrostatic-atomic accelerometer for future space gravity missions
Nassim Zahzam, Bruno Christophe, Vincent Lebat, Emilie Hardy,, Phuong-Anh Huynh, No\'emie Marquet, C\'edric Blanchard, Yannick Bidel,, Alexandre Bresson, Petro Abrykosov, Thomas Gruber, Roland Pail, Ilias Daras,, Olivier Carraz

TL;DR
This paper proposes a hybrid accelerometer combining electrostatic and quantum sensors to improve long-term gravity field measurements in space, analyzing its potential benefits, limitations, and design considerations through simulations and experiments.
Contribution
It introduces a novel hybrid accelerometer concept integrating electrostatic and cold atom interferometry sensors, with performance assessment and preliminary design analysis.
Findings
Hybrid sensor shows potential for improved gravity retrieval due to long-term stability.
Performance gains are limited by aliasing effects and require advanced de-aliasing models.
Satellite rotation impacts on CAI can be mitigated using the proposed configuration.
Abstract
Long term observation of temporal Earth's gravity field with enhanced temporal and spatial resolution is a major objective for future satellite gravity missions. Improving the performance of the accelerometers present in such missions is one of the main path to explore. In this context, we propose to study an original concept of a hybrid accelerometer associating a state-of-the-art electrostatic accelerometer (EA) and a promising quantum sensor based on cold atom interferometry. To assess the performance potential of such instrument, numerical simulations have been performed to determine its impact in term of gravity field retrieval. Taking advantage of the long term stability of the cold atom interferometer (CAI), it has been shown that the reduced drift of the hybrid sensor could lead to improved gravity field retrieval. Nevertheless this gain vanishes once temporal variations of the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
