Constraining the Chemistry of Sub-cm Diagenetic Features with Curiosity's Alpha Particle X-ray Spectrometer
Bibliographic record
Abstract
The Alpha Particle X-ray Spectrometer (APXS) onboard the Mars Science Laboratory (MSL) rover Curiosity has acquired approximately 1,300 geochemical analyses since landing in 2012. The APXS utilizes a combination of X-ray fluorescence and particle-induced X-ray emission to determine the chemical composition of materials within its 15+ mm diameter field of view (FOV) [1, 2]. Diagenetic features provide a means to further understand and constrain the habitability of Curiosity's landing site, Gale crater. These features often present as veins or nodules with an areal extent on the sub-cm scale. APXS analyses of these features therefore contain a mixture of signals from the feature and host substrate.To probe the composition of these sub-FOV features, Curiosity has developed a technique whereby multiple APXS measurements are conducted in close proximity to the primary target (referred to as a raster). The data are then analyzed to not only localize APXS FOVs, mitigating arm placement uncertainty which is on the order of 1-2 cm [2], but also infer the composition of the various endmembers within the workspace. The original raster analysis method (e.g., [2, 3]) has proven useful at deconvolving the chemistry of diagenetic features from the surrounding substrate. However, this method utilizes APXS oxide data as the primary input. These data are derived assuming a homogeneous sample for the purposes of calculating and correcting for matrix effects (the attenuation of induced X-rays by other elements in the sample). In instances of clear chemical heterogeneities, these matrix corrections can result in skewed compositions of the derived endmembers, such as a vein or nodule.Here we present an improvement to this method whereby we utilize low-level data products and isolate matrix effect calculations for each individual endmember (e.g., [4]). The derived results show significant improvements (10-30%) compared to the oxide method in stoichiometric ratios when applied to calcium sulfate veins, an ideal proof-of-concept sample. Subsequent analyses of magnesium-sulfate dominated nodules hint at other potential mobile elements within the fluids present during diagenesis, such as P, Mn, Ni, and/or Zn. Similar elements were enriched in nodules at the Ayton/Groken field site, where P2O5 and MnO concentrations in the nodular material totaled over 25 wt% at a ~2:1 P:Mn molar ratio [4]. The improved analytical method will be particularly useful as Curiosity continues to explore the Marker Band and sulfate unit.[1] Gellert & Clark (2015), Elements, 11.[2] VanBommel et al. (2016), XRS, 45.[3] VanBommel et al. (2017), XRS, 46.[4] VanBommel et al. (2023), Icarus, 392.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".