Quantitative mineral analysis of (99942) Apophis using reflectance spectroscopy
Bibliographic record
Abstract
Abstract The impact threat of some near‐Earth Asteroids (NEAs) drives our need to understand their mineral compositions. Quantitative mineral abundances based on reflectance spectroscopy are of great significance for studying the compositions of NEAs. In this study, we constrained the surface mineralogy of (99942) Apophis based on multiple diagnostic spectral parameters. The influence of non‐mineral component factors (e.g., space weathering, phase angle, and surface temperature) on diagnostic spectral parameters was evaluated. We established the connection between Apophis and corresponding meteorite analog. Our results show that the abundances of olivine and pyroxene on the surface of Apophis are 53.4 ± 6 wt% and 35.6 ± 2 wt%, respectively. The 1 μm band width is basically unaffected by phase‐angle changes and is less affected by temperature variations. Low temperature has more obvious effects on the 1.25/1 μm band depth ratio (BDR 1.25) based on the present data. When the phase angle ranges from 60° to 120°, the BDR 1.25 changes significantly with the increase or decrease of phase angle. In terms of spectral characteristics, the best meteorite analog of Apophis is LL chondrite, confirming earlier interpretations. Mineral analyses based on multiple diagnostic spectral parameters provide more consistent results. Knowledge of the surface compositions of Apophis can also inform optimum or possible defense strategies for it and other NEAs.
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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 | 0.000 |
| 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) | 0.001 | 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 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".