The growth and enrichment of intragroup gas
Bibliographic record
Abstract
The thermal and chemical properties of the hot diffuse intragroup medium (IGrM) provide important constraints on the feedback processes associated with massive galaxy formation and evolution. Here we explore these constraints via a detailed analysis of the global properties of simulated z < 3 galaxy groups drawn from a cosmological simulation that includes a well-constrained prescription for momentum-driven, stellar/supernova-powered galactic outflows but no active galactic nucleus (AGN) feedback. Our simulation successfully reproduces the key observed IGrM properties, including X-ray trends, for all but the most massive groups. The z < 1 redshift evolution of these is also consistent with the observations. Contrary to expectations, the simulated groups’ IGrM does not suffer catastrophic cooling. Yet, the z = 0 group stellar mass is ∼2× too large. We show that this is due to the build-up of cold gas in the massive galaxies before they are incorporated inside groups. This indicates that other feedback mechanisms must activate in real galaxies once they grow to M* ≈ a few ×1010 M⊙ and that these must be powerful enough to expel a significant fraction of the gas from the galactic haloes. Gentle ‘maintenance-mode’ AGN feedback would be insufficient to bring the stellar and baryonic fractions into agreement with the observations. Just as importantly, we find that the stellar/supernova-powered winds are essential for understanding the IGrM metal abundances. Our simulation is able to reproduce the observed relationship between the global IGrM iron and silicon abundances and the group X-ray temperature, and these results should be relatively insensitive to the addition of AGN feedback.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".