A lower fragmentation mass scale for clumps in high redshift galaxies: a systematic numerical study
Bibliographic record
Abstract
We perform a systematic study of the effect of sub-grid physics, resolution and structural parameters on the fragmentation of gas-rich galaxy discs into massive star forming clumps due to gravitational instability. We use the state-of-the-art zoom-in cosmological hydrodynamical simulation ARGO (Fiacconi et al. 2015) to set up the initial conditions of our models, and then carry out 26 high resolution controlled SPH simulations of high-z galaxies. We find that when blast-wave feedback is included, the formation of long-lived, gravitationally bound clumps is difficult, requiring disc gas fractions of at least 50% and massive discs, which should have $V_{max} > 200$ km/s at $z \sim 2$, more massive than the typical galaxies expected at those redshifts. Clumps have typical masses $\sim 10^7 M_{\odot}$. Clumps with mass $\sim 10^8 M_{\odot}$ are rare, as they require clump-clump merging and sustained mass accretion for a few orbital times, while normally clumps migrate inward and are tidally disrupted on the way on shorter timescales. Clump sizes are in the range $100-400$ pc. Giant clumps identified in observations ($10^8-10^9 M_{\odot}$) might either have a different origin, such as minor mergers and clumpy gas accretion, or their sizes and masses may be overestimated due to blending caused by insufficient resolution. Using an analytical model originally developed to explain the fragmentation scale in gravitationally unstable 3D protoplanetary discs, we can predict the characteristic masses of clumps soon after fragmentation much better than using the conventional Toomre mass. Due to their modest size, clumps have little effect on bulge growth as they migrate to the center. In our unstable discs a small bulge can form irrespective of the presence of long-lived clumps since it is triggered by efficient gas inflows due to global non-axisymmetric instabilities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".