THE VAST POPULATION OF WOLF-RAYET AND RED SUPERGIANT STARS IN M101. I. MOTIVATION AND FIRST RESULTS
Bibliographic record
Abstract
Assembling a catalog of at least 10,000 Wolf–Rayet (W-R) stars is an essential step in proving (or disproving) that these stars are the progenitors of Type Ib and Type Ic supernovae. To this end, we have used the Hubble Space Telescope ( HST ) to carry out a deep, He ii optical narrowband imaging survey of the ScI spiral galaxy M101. Almost the entire galaxy was imaged with the unprecedented depth and resolution that only the HST affords. Differenced with archival broadband images, the narrowband images allow us to detect much of the W-R star population of M101. We describe the extent of the survey and our images, as well as our data reduction procedures. A detailed broadband–narrowband imaging study of a field east of the center of M101, containing the giant star-forming region NGC 5462, demonstrates our completeness limits, how we find W-R candidates, their properties and spatial distribution, and how we rule out most contaminants. We use the broadband images to locate luminous red supergiant (RSG) candidates. The spatial distributions of the W-R and RSG stars near NGC 5462 are strikingly different. W-R stars dominate the complex core, while RSGs dominate the complex halo. Future papers in this series will describe and catalog more than a thousand W-R and RSG candidates that are detectable in our images, as well as spectra of many of those candidates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".