Spins and shapes of 11 near-Earth asteroids observed within the NEOROCKS project
Bibliographic record
Abstract
Context. The discovery rate of near-Earth asteroids (NEAs) has steadily increased over the past three decades, yet the physical characterization of these objects has not kept pace. Aims. In an effort to help address this gap, we combined targeted photometric observations, archival data, and sparse photometric data from the Asteroid Terrestrial-impact Last Alert System (ATLAS) survey to extract as much information as possible about NEAs’ rotation rates, spin-axis orientations, and shapes. Methods. We selected 17 NEAs with a potential for shape reconstruction and applied the light curve inversion method to derive their sidereal rotation periods, spin-axis directions, and convex shape models. Results. We successfully determined unique spin and shape models for seven NEAs: (5189) 1990 UQ, (6569) Ondaatje, (7025) 1993 QA, (8566) 1996 EN, (86450) 2000 CK33, the Hayabusa2# flyby target (98943) 2001 CC21, and (512245) 2016 AU8. For an additional four asteroids – (66251) 1999GJ2, (137199) 1999KX4, (276786) 2004 KD1, and (495615) 2015 PQ291 – we constrained their sidereal periods, spin-axis orientations, and in some cases, their shapes. Conclusions. This study highlights the importance of integrating new photometric data with archival dense light curves and sparse observations to improve the physical characterization of NEAs, even when working with suboptimal datasets. We constructed 11 NEA models, contributing to the limited set of a few dozen models derived from space missions, radar observations, and light curve inversions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".