A SENSITIVE IDENTIFICATION OF WARM DEBRIS DISKS IN THE SOLAR NEIGHBORHOOD THROUGH PRECISE CALIBRATION OF SATURATED <i>WISE</i> PHOTOMETRY
Bibliographic record
Abstract
We present a sensitive search for WISE W 3 (12 μm) and W 4 (22 μm) excesses from warm optically thin dust around Hipparcos main sequence stars within 75 pc from the Sun. We use contemporaneously measured photometry from WISE , remove sources of contamination, and derive and apply corrections to saturated fluxes to attain optimal sensitivity to >10 μm excesses. We use data from the WISE All-Sky Survey Catalog rather than the AllWISE release because we find that its saturated photometry is better behaved, allowing us to detect small excesses even around saturated stars in WISE . Our new discoveries increase by 45% the number of stars with warm dusty excesses and expand the number of known debris disks (with excess at any wavelength) within 75 pc by 29%. We identify 220 Hipparcos debris disk host stars, 108 of which are new detections at any wavelength. We present the first measurement of a 12 μm and/or 22 μm excess for 10 stars with previously known cold (50–100 K) disks. We also find five new stars with small but significant W 3 excesses, adding to the small population of known exozodi, and we detect evidence for a W 2 excess around HIP 96562 (F2V), indicative of tenuous hot (780 K) dust. As a result of our WISE study, the number of debris disks with known 10–30 μm excesses within 75 pc (379) has now surpassed the number of disks with known >30 μm excesses (289, with 171 in common), even if the latter have been found to have a higher occurrence rate in unbiased samples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".