Adaptive sampling with PIXL on the Mars Perseverance rover
Bibliographic record
Abstract
Planetary rovers can use onboard data analysis to adapt their measurement plan on the fly, improving the science value of data collected between commands from Earth. This paper describes the implementation of an adaptive sampling algorithm used by PIXL, the X-ray fluorescence spectrometer of the Mars 2020 Perseverance rover. PIXL is deployed using the rover arm to measure X-ray spectra of rocks with a scan density of several thousand points over an area of typically 5 × 7 mm. The adaptive sampling algorithm is programmed to recognize points of interest and to increase the signal-to-noise ratio at those locations by performing longer integrations. Two approaches are used to formulate the sampling rules based on past quantification data: (1) Expressions that isolate particular regions within a ternary compositional diagram, and (2) Machine learning rules that threshold for a high weight percent of particular compounds. The design of the rulesets are outlined and the performance of the algorithm is quantified using measurements from the surface of Mars. To our knowledge, PIXL’s adaptive sampling represents the first autonomous decision-making based on real-time compositional analysis by a spacecraft on the surface of another planet. • First autonomous decision-making demonstrated from real-time compositional analysis. • Adaptive sampling rules are created from X-ray spectra of targets observed on Mars. • Rules are formulated through machine learning and the analysis of ternary diagrams. • Ground-based simulations exactly reproduce the behavior observed on Mars. • Long dwells are reliably triggered for carbonates, phosphate minerals, and spinels.
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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 | 0.001 |
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 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".