UNDER PRESSURE: STAR CLUSTERS AND THE NEUTRAL HYDROGEN MEDIUM OF TIDAL TAILS
Bibliographic record
Abstract
Using archival data from ATCA, WHISP, and the Very Large Array, we have analyzed the H i emission of 22 tidal tail regions of the Mullan et al. sample of pairwise interacting galaxies. We have measured the column densities, line-of-sight velocity dispersions, and kinetic energy densities on ∼kpc scales. We also constructed a tracer of the line-of-sight velocity gradient over ∼10 kpc scales. We compared the distributions of these properties between regions that do and do not contain massive star cluster candidates ( M V < −8.5; ∼10 4 –10 6 M ☉ as observed in Hubble Space Telescope WFPC2 VI data). In agreement with Maybhate et al., we find that a local, ∼kpc-scale column density of log ≳ 20.6 cm −2 is frequently required for detecting clustered star formation. This H i gas also tends to be turbulent, with line-of-sight velocity dispersions σ los ≈ 10–75 km s −1 , implying high kinetic energy densities (log Σ KE > 46 erg pc −2 ). Thus, high H i densities and pressures, partly determined by the tail dynamical age and other interaction characteristics, are connected to large-scale cluster formation in tidal tails overall. Last, we find that the high mechanical energy densities of the gas are likely not generally due to feedback from star formation. Rather, these properties are more likely to be a cause of star formation than a result.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".