An H i threshold for star cluster formation in tidal debris*
Bibliographic record
Abstract
Superstar clusters are young, compact star clusters found in the central regions of interacting galaxies. Recently, they have also been reported to preferentially form in certain tidal tails, but not in others. In this paper, we have used 21-cm H i maps and the Hubble Space Telescope Wide Field Planetary Camera 2 images of eight tidal tail regions of four merging galaxy pairs to compare the kiloparsec scale H i distribution with the location of superstar clusters found from the optical images. For most of the tails, we find that there is an increase in superstar cluster density with increasing projected H i column density, such that the star cluster density is highest when log NH i≳ 20.6 cm−2, but equal to the background count rate at lower H i column density. However, for two tails (NGC 4038/39 Pos A and 3921), there is no significant star cluster population despite the presence of gas at high column density. This implies that the NH i threshold is a necessary but not sufficient condition for cluster formation. Gas volume density is likely to provide a more direct criterion for cluster formation, and other factors such as gas pressure or strength of encounter may also have an influence. Comparison of H i thresholds needed for formation of different types of stellar structures await higher resolution H i and optical observations of larger numbers of interacting galaxies.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".