The Meter‐Scale Roughness of Asteroid (101955) Bennu From the OSIRIS‐REx Laser Altimeter
Bibliographic record
Abstract
Abstract Asteroid (101955) Bennu is a near‐Earth, potentially hazardous, rubble pile asteroid, and was the primary target of the NASA OSIRIS‐REx mission. The surface is dominated by the expression of boulders and has been heavily modified by impact cratering. Here, we analyze surface roughness, calculated using data from the OSIRIS‐REx Laser Altimeter, to investigate spatial variations in boulders and finer‐grained material across Bennu globally. Surface roughness is a statistical measure of change in surface height over a given baseline (horizontal spatial scale) and can be used to gain insight into the geologic processes that form and modify the surface over different scales. We calculate surface roughness at baselines of 0.20–20 m using the root‐mean‐square (RMS) deviation. We find that Bennu's surface roughness is self‐affine over length scales between 0.2 m and 1.0 m, and between 1.0 and 20.0 m. We also find that surface roughness varies spatially and is dominated by the local size‐frequency distribution of boulders. At the longest baselines, roughness is produced by the prominent equatorial ridge and the topographic relief of Bennu's largest boulders. At baselines between 0.20 and 2.0 m, the interiors of craters with diameters less than 25 m tend to be smooth compared with larger craters and the average background, supporting the presence of a finer‐grained subsurface layer. Our results extend previous LiDAR‐based asteroid roughness studies of (25143) Itokawa and (433) Eros to baselines more than 10 times shorter, and to an asteroid with different spectral class.
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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 | 0.000 | 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".