A tale of two tails: exploring stellar populations in the tidal tails of NGC 3256
Bibliographic record
Abstract
We have developed an observing programme using deep, multiband imaging to probe the chaotic regions of tidal tails in search of an underlying stellar population, using NGC 3256's 400 Myr twin tidal tails as a case study. These tails have different colours of u − g = 1.05 ± 0.07 and r − i = 0.13 ± 0.07 for NGC 3256W, and u − g = 1.26 ± 0.07 and r − i = 0.26 ± 0.07 for NGC 3256E, indicating different stellar populations. These colours correspond to simple stellar population ages of |$288^{+11}_{-54}$| and |$841^{+125}_{-157}$| Myr for NGC 3256W and NGC 3256E, respectively, suggesting that NGC 3256W's diffuse light is dominated by stars formed after the interaction, while light in NGC 3256E is primarily from stars that originated in the host galaxy. Using a mixed stellar population model, we break our diffuse light into two populations: one at 10 Gyr, representing stars pulled from the host galaxies, and a younger component, whose age is determined by fitting the model to the data. We find similar ages for the young populations of both tails (|$195^{-13}_{+0}$| and |$170^{-70}_{+44}$| Myr for NGC 3256W and NGC 3256E, respectively), but a larger percentage of mass in the 10 Gyr population for NGC 3256E (|$98^{+1}_{-3}\hbox{ per cent}$| versus |$90^{+5}_{-6}\hbox{ per cent}$|). Additionally, we detect 31 star cluster candidates in NGC 3256W and 19 in NGC 2356E, with median ages of 141 and 91 Myr, respectively. NGC 3256E contains several young (<10 Myr), low-mass objects with strong nebular emission, indicating a small, recent burst of star formation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".