The inefficiency of stellar feedback in driving galactic outflows in massive galaxies at high redshift
Bibliographic record
Abstract
ABSTRACT Recent observations indicate that galactic outflows are ubiquitous in high-redshift (high-z) galaxies, including normal star-forming galaxies, quasar hosts, and dusty star-forming galaxies (DSFGs). However, the impact of outflows on the evolution of their hosts is still an open question. Here, we analyse the star-formation histories and galactic outflow properties of galaxies in massive haloes ($10^{12}\, {\rm M}_{\odot }\ \lt\ M_{\rm vir}\ \lt\ 5\times 10^{12}\, {\rm M}_{\odot }$) at z ≳ 5.5 in three zoom-in cosmological simulations from the MassiveFIRE suite, as part of the Feedback In Realistic Environments (FIRE) project. The simulations were run with the FIRE-2 model, which does not include feedback from active galactic nuclei. The simulated galaxies resemble z > 4 DSFGs, with star-formation rates of $\sim\!{1000}\ {\rm M}_{\odot }\, \rm yr^{-1}$ and molecular gas masses of Mmol ∼ 1010 M⊙. However, the simulated galaxies are characterized by higher circular velocities than those observed in high-z DSFGs. The mass loading factors from stellar feedback are of the order of ∼0.1, implying that stellar feedback is inefficient in driving galactic outflows and gas is consumed by star formation on much shorter time-scales than it is expelled from the interstellar medium. We also find that stellar feedback is highly inefficient in self-regulating star formation in this regime, with an average integrated star formation efficiency (SFE) per dynamical time of 30 per cent. Finally, compared with FIRE-2 galaxies hosted in similarly massive haloes at lower redshift, we find lower mass loading factors and higher SFEs in the high-z sample. We argue that both effects originate from the higher total and gas surface densities that characterize high-z massive systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".