Redistributing hot gas around galaxies: do cool clouds signal a solution to the overcooling problem?
Bibliographic record
Abstract
We present a pair of high-resolution smoothed particle hydrodynamics simulations that explore the evolution and cooling behaviour of hot gas around Milky Way size galaxies. The simulations contain the same total baryonic mass and are identical other than their initial gas density distributions. The first is initialized with a low-entropy hot gas halo that traces the cuspy profile of the dark matter, and the second is initialized with a high-entropy hot halo with a cored density profile as might be expected in models with pre-heating feedback. Galaxy formation proceeds in dramatically different fashion depending on the initial setup. While the low-entropy halo cools rapidly, primarily from the central region, the high-entropy halo is quasi-stable for ∼4 Gyr and eventually cools via the fragmentation and infall of clouds from ∼100 kpc distances. The low-entropy halo's X-ray surface brightness is ∼100 times brighter than current limits and the resultant disc galaxy contains more than half of the system's baryons. The high-entropy halo has an X-ray brightness that is in line with observations, an extended distribution of pressure-confined clouds reminiscent of observed populations and a final disc galaxy that has half the mass and ∼50 per cent more specific angular momentum than the disc formed in the low-entropy simulation. The final high-entropy system retains the majority of its baryons in a low-density hot halo. The hot halo harbours a trace population of cool, mostly ionized, pressure-confined clouds that contain ∼10 per cent of the halo's baryons after 10 Gyr of cooling. The covering fraction for H i and Mg ii absorption clouds in the high-entropy halo is ∼0.4 and ∼0.6, respectively, although most of the mass that fuels disc growth is ionized, and hence would be under counted in H i surveys.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".