Mass and Shape Determination of (101955) Bennu Using Differenced Data from Multiple OSIRIS-REx Mission Phases
Bibliographic record
Abstract
Abstract The Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) mission collected a sample from the rubble-pile asteroid (101955) Bennu for return to Earth. For the successful Touch And Go sample acquisition maneuver, the shape and mass of the asteroid needed to be known precisely. Here we use a combination of radiometric, image landmark, and laser altimetry data to determine Bennu’s mass, shape, and orientation simultaneously and to verify existing models thereof. Our shape determination consists of estimating a scale factor and three frame rotation angles that apply to both the global digital terrain model (GDTM) and the landmark coordinates. We use a data type called image constraints, where we take the difference of the observation of the same landmark in images taken at two different times. We analyze data from two phases of the OSIRIS-REx mission, Orbital B and Recon B, and show that interphase image constraints greatly reduce interdependencies between estimated parameters for mass, GDTM scale, and biases on the altimetry data. This results in an improved solution for the mass and shape relative to considering a single mission phase. We find Bennu’s gravitational parameter GM to be 4.89256 ± 0.00035 m3 s−2, and we find a scale factor of 1.000896 ± 0.00036 for the altimetry-based GDTM. Using the scaled volume, this results in a bulk density of 1191.57 ± 1.74 kg m−3 , which is within the uncertainties of previous analyses but more precise.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".