Star clusters in tidal debris
Bibliographic record
Abstract
ABSTRACT We present results of a Hubble Space Telescope (HST) UBVI-band study of star clusters in tidal tails, using new WFC3 and ACS imaging to complement existing WFPC2 data. We survey 12 tidal tails across seven merging systems, deriving ages and masses for 425 star cluster candidates (SCCs). The stacked mass distribution across all systems follows a power law of the form dN/dM ∝ Mβ, with β = −2.02 ± 0.15, consistent with what is seen in other star-forming environments. GALEX and Swift UV imaging provide star formation rates (SFRs) for our tidal tails, which when compared with ages and masses of our SCCs, allows for a determination of the cluster formation efficiency (CFE). We find the CFE increases with increasing SFR surface density, matching the theoretical model. We confirm this fit down at SFR densities lower than previously measured (log ΣSFR (M⊙ yr−1 kpc−2) ≈ −4.2), as related to the CFE. We determine the half-light radii for a refined sample of 57 SCCs with our HST WFC3 and ACS imaging, and calculate their dynamical age, finding the majority of them to be gravitationally bound. We also provide evidence of only low-mass (<104 M⊙) cluster formation in our nearest galaxy, NGC 1487, consistent with the theory that this system is a dwarf merger.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".