THE FAINTEST WISE DEBRIS DISKS: ENHANCED METHODS FOR DETECTION AND VERIFICATION
Bibliographic record
Abstract
ABSTRACT In an earlier study, we reported nearly 100 previously unknown dusty debris disks around Hipparcos main-sequence stars within 75 pc by selecting stars with excesses in individual WISE colors. Here, we further scrutinize the Hipparcos 75 pc sample to (1) gain sensitivity to previously undetected, fainter mid-IR excesses and (2) remove spurious excesses contaminated by previously unidentified blended sources. We improve on our previous method by adopting a more accurate measure of the confidence threshold for excess detection and by adding an optimally weighted color average that incorporates all shorter-wavelength WISE photometry, rather than using only individual WISE colors. The latter is equivalent to spectral energy distribution fitting, but only over WISE bandpasses. In addition, we leverage the higher-resolution WISE images available through the unWISE.me image service to identify contaminated WISE excesses based on photocenter offsets among the W3- and W4-band images. Altogether, we identify 19 previously unreported candidate debris disks. Combined with the results from our earlier study, we have found a total of 107 new debris disks around 75 pc Hipparcos main-sequence stars using precisely calibrated WISE photometry. This expands the 75 pc debris disk sample by 22% around Hipparcos main-sequence stars and by 20% overall (including non-main-sequence and non-Hipparcos stars).
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".