Shedding Light on the Isolation of Luminous Blue Variables
Bibliographic record
Abstract
Abstract In the standard view of massive star evolution, luminous blue variables (LBVs) are transitional objects between the most massive O-type stars and Wolf–Rayet (WR) stars. With short lifetimes, these stars should all be found near one another. A recent study of LBVs in the Large Magellanic Cloud (LMC) found instead that LBVs are considerably more isolated than either O-type stars or WRs, with a distribution intermediate between that of the WRs and red supergiants (RSGs). A similar study, using a more restricted sample of LBVs, reached the opposite conclusion. Both studies relied upon the distance to the nearest spectroscopically identified O-type star to define the degree of isolation. However, our knowledge of the spectroscopic content of the LMC is quite spotty. Here we re-examine the issue using carefully defined photometric criteria to select the highest-mass unevolved stars (“bright blue stars,” or BBSs), using spatially complete photometric catalogs of the LMC, M31, and M33. Our study finds that the LBVs are no more isolated than BBSs or WRs. This result holds no matter which sample of LBVs we employ. A statistical test shows that we can rule out the LBVs having the same distribution as the RSGs, which are about 2× more isolated. We demonstrate the robustness of our results using the second-closest neighbor. Furthermore, the majority of LBVs in the LMC are found in or near OB associations as are the BBS and WRs; the RSGs are not. We conclude that the spatial distribution of LBVs therefore is consistent with the standard picture of massive star evolution.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".