A lower fragmentation mass scale in high-redshift galaxies and its implications on giant clumps: a systematic numerical study
Bibliographic record
Abstract
We study the effect of sub-grid physics, galaxy mass, structural parameters and resolution on the fragmentation of gas-rich galaxy discs into massive star-forming clumps. The initial conditions are set up with the aid of the ARGO cosmological hydrodynamical simulation. Blast-wave feedback does not suppress fragmentation, but reduces both the number of clumps and the duration of the unstable phase. Once formed, bound clumps cannot be destroyed by our feedback model. Widespread fragmentation is promoted by high gas fractions and low halo concentrations. Yet giant clumps M > 108 M⊙ lasting several hundred Myr are rare and mainly produced by clump–clump mergers. They occur in massive discs with maximum rotational velocities Vmax > 250 km s−1 at z ∼ 2, at the high-mass end of the observed galaxy population at those redshifts. The typical gaseous and stellar masses of clumps in all runs are in the range ∼107–108 M⊙ for galaxies with disc mass in the range 1010–8 × 1010 M⊙. Clumps sizes are usually in the range 100–400 pc, in agreement with recent clump observations in lensed high-z galaxies. We argue that many of the giant clumps identified in observations are not due to in situ fragmentation, or are the result of blending of smaller structures owing to insufficient resolution. Using an analytical model describing local collapse inside spiral arms, we can predict the characteristic gaseous masses of clumps at the onset of fragmentation (∼3–5 × 107 M⊙) quite accurately, while the conventional Toomre mass overestimates them. Due to their moderate masses, clumps which migrate to the centre have marginal effect on bulge growth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".