Homogeneity assessment of the SuperCam calibration targets onboard rover perseverance
Bibliographic record
Abstract
The SuperCam instrument, onboard the Perseverance rover (Mars 2020 mission) is designed to perform remote analysis on the Martian surface employing several spectroscopic techniques such as Laser Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman (TRR), Time-Resolved Fluorescence (TRF) and Visible and Infrared (VISIR) reflectance. In addition, SuperCam also acquires high-resolution images using a color remote micro-imager (RMI) as well as sounds with its microphone. SuperCam has three main subsystems, the Mast Unit (MU) where the laser for chemical analysis and collection optics are housed, the Body Unit (BU) where the different spectrometers are located inside the rover, and the SuperCam Calibration Target (SCCT) located on the rover's deck to facilitate calibration tests at similar ambient conditions as the analyzed samples. To perform adequate calibrations on Mars, the 22 mineral samples included in the complex SCCT assembly must have a very homogeneous distribution of major and minor elements. The analysis and verification of such homogeneity for the 5-6 replicates of the samples included in the SCCT has been the aim of this work. To verify the physic-chemical homogeneity of the calibration targets, micro Energy Dispersive X-ray Fluorescence (EDXRF) imaging was first used on the whole surface of the targets, then the relative abundances of the detected elements were computed on 20 randomly distributed areas of 100 × 100 μm. For those targets showing a positive Raman response, micro-Raman spectroscopy imaging was performed on the whole surface of the targets at a resolution of 100 × 100 μm. The %RSD values (percent of relative standard deviation of mean values) for the major elements measured with EDXRF were compared with similar values obtained by two independent LIBS set-ups at spot sizes of 300 μm in diameter. The statistical analysis showed which elements were homogeneously distributed in the 22 mineral targets of the SCCT, providing their uncertainty values for further calibration. Moreover, nine of the 22 targets showed a good Raman response and their mineral distributions were also studied. Those targets can be also used for calibration purposes of the Raman part of SuperCam using the wavenumbers of their main Raman bands proposed in this work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".