CLEANING THE USNO-B CATALOG THROUGH AUTOMATIC DETECTION OF OPTICAL ARTIFACTS
Bibliographic record
Abstract
The USNO-B Catalog contains spurious entries that are caused by diffraction spikes and circular reflection halos around bright stars in the original imaging data. These spurious entries appear in the Catalog as if they were real stars; they are confusing for some scientific tasks. The spurious entries can be identified by simple computer vision techniques because they produce repeatable patterns on the sky. Some techniques employed here are variants of the Hough transform, one of which is sensitive to (two-dimensional) overdensities of faint stars in thin right-angle cross patterns centered on bright (<13 mag) stars, and one of which is sensitive to thin annular overdensities centered on very bright (<7 mag) stars. After enforcing conservative statistical requirements on spurious-entry identifications, we find that of the 1,042,618,261 entries in the USNO-B Catalog, 24,148,382 (2.3 percent) are identified as spurious by diffraction-spike criteria and 196,133 (0.02 percent) are identified as spurious by reflection-halo criteria. The spurious entries are often detected in more than two bands and are not overwhelmingly outliers in any photometric properties; they therefore cannot be rejected easily on other grounds, i.e., without the use of computer vision techniques. We demonstrate our method, and return to the community in electronic form a table of spurious entries in the Catalog.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.022 |
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".