STAR CLUSTERS IN THE TIDAL TAILS OF INTERACTING GALAXIES: CLUSTER POPULATIONS ACROSS A VARIETY OF TAIL ENVIRONMENTS
Bibliographic record
Abstract
We have searched for compact stellar structures within 17 tidal tails in 13 different interacting galaxies using F606W - and F814W -band images from the Wide Field Planetary Camera 2 on the Hubble Space Telescope . The sample of tidal tails includes a diverse population of optical properties, merging galaxy mass ratios, H i content, and ages. Combining our tail sample with Knierman et al., we find evidence of star clusters formed in situ with M V < −8.5 and V − I < 2.0 in 10 of 23 tidal tails; we are able to identify cluster candidates to M V = −6.5 in the closest tails. Three tails offer clear examples of "beads on a string" star formation morphology in V − I color maps. Two tails present both tidal dwarf galaxy candidates and cluster candidates. Statistical diagnostics indicate that clusters in tidal tails may be drawn from the same power-law luminosity functions (with logarithmic slopes ≈−2 to −2.5) found in quiescent spiral galaxies and interiors of interacting systems. We find that the tail regions with the largest number of observable clusters are relatively young (≲250 Myr old) and bright ( V ≲ 24 mag arcsec −2 ), probably attributed to the strong bursts of star formation in interacting systems soon after periapse. Otherwise, we find no statistical difference between cluster-rich and cluster-poor tails in terms of many observable characteristics, though this analysis suffers from complex, unresolved gas dynamics and projection effects.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".