Profiling Martian Dust Using PIXL Images
Bibliographic record
Abstract
The Planetary Instrument of X-ray Lithochemistry (PIXL), onboard the Mars 2020 rover Perseverance, is designed to measure the chemical composition of Martian materials with a spatial resolution of around 100 µm. The surface of Mars is notoriously dusty and even thin layers of dust within the measurement frame will impact the instrument signal, potentially leading to misinterpretation of an underlying chemical composition if not appropriately accounted for. Therefore, knowledge about the dust composition, concentration and distribution is important, both when deciding where to perform measurements and in data analyses. Herein we present methods for generating high precision dust profiles of Martian surfaces by utilizing the Optical Fiducial System (OFS) component of the PIXL instrument. The OFS consist of a Micro Context Camera (MCC) and a FloodLight Illuminator (FLI). The MCC captures images with a resolution better than 50 µm/pixel at the instrument’s nominal distance of 25 mm, directly enabling the optical characterization of dust, and other components, on these surfaces. The FLI is equipped with a total of 24 light emitting diodes (LEDs), in four groups centered at UV (385 nm), Blue (450 nm), Green (530 nm), and NIR (735 nm), enabling multispectral capabilities. This multispectral floodlight capability directly facilitates dust detection by the MCC, and utilizing the precision of the MCC, the spatial distribution of dust is better constrained. We present dust profile maps acquired by the PIXL OFS on Martian surfaces and present similar results derived from the rover-mounted calibration targets. In demonstrating the quality of the maps produced, we can improve future scientific analyses while furthermore improving the operational efficiency and data quality of the Perseverance mission through the potential future implementation of a closed-loop autonomous dust-avoidance routine, utilizing the macro capabilities of the PIXL OFS. The author recognises the great contribution made by the PIXL Team and the broader Mars 2020 Team.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".