CHARACTERIZING WOLF-RAYET STARS IN THE NEAR- AND MID-INFRARED
Bibliographic record
Abstract
We present refined color–color selection criteria for identifying Wolf–Rayet (WR) stars using available mid-infrared (MIR) photometry from WISE in combination with near-infrared (NIR) photometry from the Two Micron All Sky Survey. Using a sample of spectrally classified objects, we find that WR stars are well distinguished from the field stellar population in the ( W 1 − W 2) versus ( J − K s ) color–color diagram, and further distinguished from other emission line objects such as planetary nebulae, Be, and cataclysmic variable stars using a combination of NIR and MIR color constraints. As proof of concept we applied the color constraints to a photometric sample in the Galactic plane, located WR star candidates, and present five new spectrally confirmed and classified WC (1) and WN (4) stars. Analysis of the 0.8–5.0 μm spectral data for a subset of known, bright WC and WN stars shows that emission lines (primarily He i ) extend into the 3.0–5.0 μm spectral region, although their strength is greatly diminished compared to the 0.8–2.5 μm region. The WR population stands out relative to background field stars at NIR and MIR colors due to an excess continuum contribution, likely caused by free–free scattering in dense winds. Mean photometric properties of known WRs are presented and imply that reddened late-type WN and WC sources are easier to detect than earlier-type sources at larger Galactic radii. WISE W 3 and W 4 images of 10 WR stars show evidence of circumstellar shells linked to mass ejections from strong stellar winds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".