Asteroid Occultations with VERITAS: Observations and Considerations
Bibliographic record
Abstract
Occultations, the covering up of one celestial body by another celestial body, have been used in astronomy for millennia to learn about the sun and moon. Since 2018, VERITAS has implemented a program to detect predicted asteroid occultations, where an asteroid covers up a star. VERITAS has attempted to observe over 100 occultations to date and successfully observed 20 occultations. With these occultations, VERITAS can directly measure the smallest angular diameters of any instrument or technique in the optical for stars between magnitude 9 and 13. Each angular diameter is measured by fitting the diffraction pattern observed by the central VERITAS pixel at the start and end of an occultation. Once a planned FADC upgrade is complete, VERITAS will begin a program to search for serendipitous occultations within its full field of view (3 deg). Serendipitous occultations of sub-km trans-Neptunian objects (TNOs) have the potential to constrain models of solar system formation. This presentation will detail how VERITAS predicts and observes occultations as well as the overall status of the asteroid occultation program and future steps for observing occultations of both asteroids and TNOs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".