Stellar feedback in a clumpy galaxy at <i>z</i> ∼ 3.4
Bibliographic record
Abstract
ABSTRACT Giant star-forming regions (clumps) are widespread features of galaxies at z ≈ 1−4. Theory predicts that they can play a crucial role in galaxy evolution, if they survive to stellar feedback for >50 Myr. Numerical simulations show that clumps’ survival depends on the stellar feedback recipes that are adopted. Up to date, observational constraints on both clumps’ outflows strength and gas removal time-scale are still uncertain. In this context, we study a line-emitting galaxy at redshift z ≃ 3.4 lensed by the foreground galaxy cluster Abell 2895. Four compact clumps with sizes ≲280 pc and representative of the low-mass end of clumps’ mass distribution (stellar masses ≲2 × 108 M⊙) dominate the galaxy morphology. The clumps are likely forming stars in a starbursting mode and have a young stellar population (∼10 Myr). The properties of the Lyman-α (Lyα) emission and nebular far-ultraviolet absorption lines indicate the presence of ejected material with global outflowing velocities of ∼200–300 km s−1. Assuming that the detected outflows are the consequence of star formation feedback, we infer an average mass loading factor (η) for the clumps of ∼1.8–2.4 consistent with results obtained from hydrodynamical simulations of clumpy galaxies that assume relatively strong stellar feedback. Assuming no gas inflows (semiclosed box model), the estimates of η suggest that the time-scale over which the outflows expel the molecular gas reservoir (≃7 × 108 M⊙) of the four detected low-mass clumps is ≲50 Myr.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".