Raman Characterization of the CanMars Rover Field Campaign Samples Using the Raman Laser Spectrometer ExoMars Simulator: Implications for Mars and Planetary Exploration
Bibliographic record
Abstract
The Mars 2020 Perseverance rover landed on February 18, 2021, and has started ground operations. The ExoMars Rosalind Franklin rover will touch down on June 10, 2023. Perseverance will be the first-ever Mars sample caching mission—a first step in sample return to Earth. SuperCam and Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC) on Perseverance, and Raman Laser Spectrometer (RLS) on Rosalind Franklin, will comprise the first ever in situ planetary mission Raman spectroscopy instruments to identify rocks, minerals, and potential organic biosignatures on Mars' surface. There are many challenges associated when using Raman instruments and the optimization and quantitative analysis of resulting data. To understand how best to overcome them, we performed a comprehensive Raman analysis campaign on CanMars, a Mars sample caching rover analog mission undertaken in Hanksville, Utah, USA, in 2016. The Hanksville region presents many similarities to Oxia Planum's past habitable conditions, including liquid water, flocculent, and elemental compounds (such as clays), catalysts, substrates, and energy/food sources for life. We sampled and conducted a complete band analysis of Raman spectra as mission validation analysis with the RLS ExoMars Simulator or RLS Sim, a breadboard setup representative of the ExoMars RLS instrument. RLS Sim emulates the operational behavior of RLS on the Rosalind Franklin rover. Given the high fidelity of the Mars analog site and the RLS Sim, the results presented here may provide important information useful for guiding in situ analysis and sample triage for caching relevant for the Perseverance and Rosalind Franklin missions. By using the RLS Sim on CanMars samples, our measurements detected oxides, sulfates, nitrates, carbonates, feldspars, and carotenoids, many with a higher degree of sensitivity than past results. Future work with the RLS Sim will aim to continue developing and improving the capability of the RLS system in the future ExoMars 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".