Asteroid models reconstructed from ATLAS photometry
Bibliographic record
Abstract
Context. The Asteroid Terrestrial-impact Last Alert System (ATLAS) is an all-sky survey primarily aimed at detecting potentially hazardous near-Earth asteroids. Apart from the astrometry of asteroids, it also produces their photometric measurements that contain information about asteroid rotation and their shape. Aims. To increase the current number of asteroids with a known shape and spin state, we reconstructed asteroid models from ATLAS photometry that was available for approximately 180 000 asteroids observed between 2015 and 2018. Methods. We made use of the light-curve inversion method implemented in the Asteroids@home project to process ATLAS photometry for roughly 100 000 asteroids with more than a hundred individual brightness measurements. By scanning the period and pole parameter space, we selected those best-fit models that were, according to our setup, a unique solution for the inverse problem. Results. We derived ~2750 unique models, 950 of them were already reconstructed from other data and published. The remaining 1800 models are new. About half of them are only partial models, with an unconstrained pole ecliptic longitude. Together with the shape and spin, we also determined for each modeled asteroid its color index from the cyan and orange filter used by the ATLAS survey. We also show the correlations between the color index, albedo, and slope of the phase-angle function. Conclusions. The current analysis is the first inversion of ATLAS asteroid photometry, and it is the first step in exploiting the huge scientific potential that ATLAS photometry has. ATLAS continues to observe, and in the future, this data, together with other independent photometric measurements, can be inverted to produce more refined asteroid models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".