Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer): I. On-ground setup description and characterisation
Mathieu Vincendon, Pierre Guiot, Beno\^it Lecomte, Mathieu Condamin,, Fran\c{c}ois Poulet, Antoine Arondel, Julien Barbay, John Carter, Simone De, Angelis, Cydalise Dumesnil, Gianrico Filacchione, Paolo Haffoud, J\'er\'emie, Hansotte, Yves Langevin, Pierre-Louis Mayeur

TL;DR
This paper details the on-ground calibration setup and performance characterization of the MAJIS instrument for the JUICE mission, ensuring accurate surface and atmosphere measurements of Jupiter's moons.
Contribution
It introduces a comprehensive calibration setup for MAJIS, including methods to mitigate thermal infrared emissions and validate instrument performance before launch.
Findings
Calibration setup effectively characterized radiometric, spectral, and geometric properties.
Mitigation techniques reduced thermal infrared emission effects.
Performance validation confirmed readiness for space observations.
Abstract
The visible and infrared Moon And Jupiter Imaging Spectrometer (MAJIS), aboard the JUpiter ICy Moons Explorer (JUICE) spacecraft, will characterize the composition of the surfaces and atmospheres of the Jupiter system. Prior to the launch, a campaign was carried out to obtain the measurements needed to calibrate the instrument. The aim was not only to produce data for the calculation of the radiometric, spectral, and spatial transfer functions, but also to evaluate MAJIS performance, such as signal-to-noise ratio and amount of straylight, under near-flight conditions. Here, we first describe the setup implemented to obtain these measurements, based on five optical channels. We notably emphasize the concepts used to mitigate thermal infrared emissions generated at ambient temperatures, since the MAJIS spectral range extends up to 5.6 m. Then, we characterize the performance 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.
