A Strategy for Finding Near-Earth Objects with the SDSS Telescope
Bibliographic record
Abstract
We present a detailed observational strategy for finding near-Earth objects (NEOs) with the Sloan Digital Sky Survey (SDSS) telescope. We investigate strategies in normal, unbinned mode, as well as binning the CCDs 2 × 2 or 3 × 3, which affects the sky coverage rate and the limiting apparent magnitude. We present results from 1 month, 3 year, and 10 year simulations of such surveys. For each cadence and binning mode, we evaluate the possibility of achieving the Spaceguard goal of detecting 90% of 1 km NEOs (absolute magnitude H ≤ 18 for an albedo of 0.1). We find that an unbinned survey is most effective at detecting H ≤ 20 NEOs in our sample. However, a 3 × 3 binned survey reaches the Spaceguard goal after only 7 years of operation. As the proposed large survey telescopes (Pan-STARRS, LSST) are at least 5–10 years from operation, an SDSS NEO survey could make a significant contribution to the detection and photometric characterization of the NEO population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".