THE VAST POPULATION OF WOLF-RAYET AND RED SUPERGIANT STARS IN M101. I. MOTIVATION AND FIRST RESULTS
Bibliographic record
Abstract
M101 is an ideal target in which to test predictions of massive star birth and evolution. The large abundance gradient across M101 (a factor of 20) suggests that many more WR stars must be found in the inner parts of this galaxy than in the outer regions. Many HII regions and massive star-forming complexes have been identified in M101; they should be rich in WR stars, and surrounded by RSG stars. Finally, the Wolf-Rayet stars in M101 may be abundant enough for one to explode as a Type Ib or Ic supernova and/or GRB within a generation. The clear identification of the progenitor of a Type Ib or Ic supernova as a WR star would be a major confirmation of current stellar evolution theory. Motivated by these considerations, we have used the Hubble Space Telescope to carry out a deep, HeII optical narrowband imaging survey of the massive star populations in the ScI spiral galaxy M101. Combined with archival broadband images, we were able to image almost the entire galaxy with the unprecedented depth and resolution that only HST affords. We describe the extent of the survey and our images, as well as our data reduction procedures. A detailed study of a field east of the center of M101, containing the giant star-forming region NGC 5462, demonstrates how we find candidates, their properties and spatial distribution, and how we rule out most contaminants. The spatial distributions of the WR and RSG stars near a giant star-forming complex are strikingly different. WR stars dominate the complex core, while RSG dominate the complex halo. Future papers in this series will describe and catalog more than a thousand WR 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".