Gas accretion and ram pressure stripping of haloes in void walls
Bibliographic record
Abstract
ABSTRACT We conduct hydrodynamical cosmological zoom simulations of 14 voids to study the ability of haloes to accrete gas at different locations throughout the voids at z = 0. Measuring the relative velocity of haloes with respect to their ambient gas, we find that one-tenth of the haloes are expected to be unable to accrete external gas due to its fast flow passed them (so called ‘fast flow haloes’). These are typically located near void walls. We determine that these haloes have recently crossed the void wall and are still moving away from it. Their motion counter to that of ambient gas falling towards the void wall results in fast flows that make external gas accretion very challenging, and often cause partial gas loss via the resultant ram pressures. Using an analytical approach, we model the impact of such ram pressures on the gas inside haloes of different masses. A halo’s external gas accretion is typically cut off, with partial stripping of halo gas. For masses below a few times 109 M⊙, their halo gas is heavily truncated but not completely stripped. We identify numerous examples of haloes with a clear jelly-fish like gas morphology, indicating their surrounding gas is being swept away, cutting them off from further external accretion. These results highlight how, even in the relatively low densities of void walls, a fraction of galaxies can interact with large-scale flows in a manner that has consequences for their gas content and ability to accrete gas.
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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 | 0.001 | 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) | 0.002 | 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".