Giant clumps in the FIRE simulations: a case study of a massive high-redshift galaxy
Bibliographic record
Abstract
The morphology of massive star-forming galaxies at high redshift is often dominated by giant clumps of mass ∼108–109 M⊙ and size ∼100–1000 pc. Previous studies have proposed that giant clumps might have an important role in the evolution of their host galaxy, particularly in building the central bulge. However, this depends on whether clumps live long enough to migrate from their original location in the disc or whether they get disrupted by their own stellar feedback before reaching the centre of the galaxy. We use cosmological hydrodynamical simulations from the FIRE (Feedback in Realistic Environments) project which implement explicit treatments of stellar feedback and interstellar medium physics to study the properties of these clumps. We follow the evolution of giant clumps in a massive (M* ∼ 1010.8 M⊙ at z = 1), discy, gas-rich galaxy from redshift z ≳ 2 to z = 1. Even though the clumpy phase of this galaxy lasts over a gigayear, individual gas clumps are short-lived, with mean lifetime of massive clumps of ∼20 Myr. During that time, they turn between 0.1 per cent and 20 per cent of their gas into stars before being disrupted, similar to local giant molecular clouds. Clumps with M ≳ 107 M⊙ account for ∼20 per cent of the total star formation in the galaxy during the clumpy phase, producing ∼1010 M⊙ of stars. We do not find evidence for net inward migration of clumps within the galaxy. The number of giant clumps and their mass decrease at lower redshifts, following the decrease in the overall gas fraction and star formation rate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".