Angular momentum transfer in cosmological simulations of Milky Way-mass discs
Bibliographic record
Abstract
ABSTRACT Fuelling star formation in large, discy galaxies requires a continuous supply of gas accreting into star-forming regions. Previously, we characterized this accretion in four Milky Way mass galaxies ($M_{\rm halo}\sim 10^{12}{\rm M}_{\odot }$) in the FIRE-2 cosmological zoom-in simulations. At $z\sim 0$, we found that gas within the inner circumgalactic medium (iCGM) approaches the disc with comparable angular momentum (AM) to the disc edge, joining in the outer half of the gaseous disc. Within the disc, gas moves inwards at velocities of $\sim$1–5 km s$^{-1}$ while fully rotationally supported. In this study, we analyse the torques that drive these flows. In all cases studied, we find that the torques in discs enable gas accreted near the disc edge to transport inwards and fuel star formation in the central few kpc. The primary sources of torque come from gravity, hydrodynamical forces, and the sub-grid $P \mathrm{ d}V$ work done by supernova (SN) remnants interacting with gas on $\lesssim$10 pc scales. These SNe remnant interactions induce negative torques within the inner disc and positive torques in the outer disc. The gas–gas gravitational, hydro, and ‘feedback’ torques transfer AM outwards to where accreting gas joins the disc, playing an important role in driving inflows and regulating disc structure. Gravitational torques from stars and dark matter provide an AM sink within the innermost regions of the disc and iCGM, respectively. Feedback torques are dominant within the disc, while gravitational and hydrodynamical torques have similar significance depending on the system/region. Torques from viscous shearing, magnetic forces, stellar winds, and radiative transfer are less significant.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".