Maximising the science return of the Galileo/NIMS dataset using a pixel-based database framework

The Galileo NIMS data set

Between 1995 and 2003, the NASA Galileo spacecraft explored the Jupiter system, collecting invaluable data on the planet and its moons. Among many instruments, the spacecraft was equipped with the Near-Infrared Mapping Spectrometer instrument (NIMS), a complex imaging spectrometer operating from 0.7 to 5.2 microns with 17 detectors [1]. Galileo NIMS data are archived in the PDS as so-called “tubes” and “g-cubes” (or mosaics). Due to the varying distance and observing geometry of each targeted flyby in the Jupiter system, together with the own instrument operational settings and health, the dataset is very heterogenous in spatial, spectral, and angular resolution. Nonetheless, the Galileo/NIMS data represent one of the most valuable resource to model and map the surface properties (composition, grain size, roughness, phase function) of Jupiter's moons, which are the prime targets of the Europa Clipper [2] and ESA JUICE [3] missions in the decade.

The NIMS database framework

We converted the Galileo/NIMS calibrated g-cube dataset publicly available in the PDS Imaging Node (as g-cubes) into a relational database, which allows to quickly select and extract radiance factors (I/F), radiance (I), geometry data, and metadata from the entire NIMS data set. The data cover the NIMS observations of Jupiter, Io, Europa, Ganymede, and Callisto. Due to the heterogenous calibration of the data set, calibration information and calibration data available from the g-cubes labels are also stored in the database. The smallest element in the database is a spectrum (i.e. one pixel). Using SQL queries on the database, and criteria defined on the pixel viewing geometry (e.g. incidence, emission, phase, and azimuth) and the geographic pixel location (latitudes and longitudes on a given target), phase curves and/or collections of spectra can be easily retrieved from regions of interest. Individual g-cubes data can also be retrieved.

 

Future steps

We are currently integrating this framework within the ESA DataLabs compute platform, which provides a JupyterLab-based environment from which users will be able to easily access the database using Python notebooks. Future updates will incorporate the NIMS tubes data to the existing g-cubes in the database. Following the recent efforts from the scientific community to reanalyse and recalibrate the NIMS data, more recent recalibrated NIMS data sets [e.g. 4, 5] may be incorporated to the framework.

References  [1] Carlson et al., Space Science Reviews, 60, 457-502, 1992 ; [2] Howell and Pappalardo, Nat Commun 11, 1311, 2020 ; [3] Grasset et al., Plan Spac Sci 78, 1-21, 2013; [4] Malaska et al., 2023a, PDART program, DOI:10.17189/4sq6-x165 ; [5] Malaska et al., 2023b, PDART program, DOI:10.17189/4sz4-5024