The SuperCam Remote Sensing Suite for MARS 2020: Nested and Co-Aligned LIBS, Raman, and VISIR Spectroscopies, and color micro-imaging
Bibliographic record
Abstract
As chartered by the Science Definition Team, the Mars 2020 mission addresses four primary objectives: A. Characterize the processes that formed and modified the geologic record within an astrobiologically relevant ancient environment, B. Perform astrobiologically relevant investigations to determine habitability, search for materials with biosignature presentation potential, and search for evidence of past life, C. Assemble a returnable cache of samples and D. Contribute to preparation for human exploration of Mars. The SuperCam instrument, selected for the Mars 2020 rover, as a suite of four instruments, provides nested and co-aligned remote investigations: Laser Induced Breakdown Spectroscopy (LIBS), Raman spectroscopy and time-resolved fluorescence (TRF), visible and near-infrared spectroscopy (VISIR), and high resolution color imaging (RMI). SuperCam appeals broadly to the four Mars 2020 objectives.In detail, SuperCam will perform:1. Microscale mineral identification by combining LIBS elemental and VISIR mineralogical spectroscopies, especially targeting secondary minerals2. Determine the sedimental stratigraphy through color imaging and LIBS and VISIR spectroscopy3. Search for organics and bio-signatures with LIBS and Raman spectroscopy4. Quantify the volatile content of the rocks by LIBS spectroscopy to determine the degree of aquaeous alteration5. Characterize the texture of the rocks by color imaging to determine their alteration processes6. Characterize the rocks' coatings by LIBS spectroscopy7. Characterize the soil and its potential for biosignature preservation8. Monitor the odd-oxygen atmospheric chemistry.To meet these goals SuperCam will perform LIBS spectroscopy on 0.5 mm spot up to 7-meter distance, perform Raman and time-resolved fluoresence up to 12-m distance with a 0.8 mrad angular resolution, a 100 ns time gating in the 534-850 nm spectral range, acquire VISIR spectra in the range 0.4-0.85 μm with a resolution of 0.35 nm, and in the IR range over 1.3-2.6 μm, rich in mineral signatures, with a resolution of 20 nm, and provide RGB images with an angular resolution of 40 μrad over a FOV of 20 mrad.We will present the science performances of SuperCam and the forecasted operation plans.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".