Multiscale and Multispectral Characterization of Mineralogy with the ExoMars 2022 Rover Remote Sensing Payload
Bibliographic record
Abstract
Abstract In 2022, the European Space Agency and Roscosmos will launch the ExoMars rover, with the scientific objective to detect evidence of life within the Martian surface via the deployment of a 2 m drill. The ExoMars Pasteur payload contains several imaging and spectroscopic instruments key to this objective: the Panoramic Camera (PanCam), Infrared Spectrometer for ExoMars (ISEM), and Close‐UP Imager (CLUPI). These instruments are able to collect data at a variety of spatial (sub‐mm to decimeter) and spectral (3.3 to 120 nm) resolutions across the 440 to 3,300 nm wavelength range and collectively will form a picture of the geological and morphological characteristics of the surface terrain surrounding the rover. We deployed emulators of this instrument suite at terrestrial analog sites that formed in a range of aqueous environments to test their ability to detect and characterize science targets. We find that the emulator suite is able to effectively detect, characterize, and refine the compositions of multiple targets at working distances spanning from 2 to 18 m. We report on (a) the detection of hydrothermal alteration minerals including Fe‐smectites and gypsum from basaltic substrates, (b) the detection of late‐stage diagenetic gypsum veins embedded in exposures of sedimentary mudstone, (c) multispectral evidence of compositional differences detected from fossiliferous mudstones, and (d) approaches to cross‐referencing multi‐scale and multi‐resolution data. These findings aid in the development of data products and analysis toolkits in advance of the ExoMars rover mission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".