Core to Cosmic Edge: SIMBA-C's New Take on Abundance Profiles in the Intragroup Medium at z = 0
Bibliographic record
Abstract
We employ the SIMBA-C cosmological simulation to study the impact of its upgraded chemical enrichment model (Chem5) on the distribution of metals in the intragroup medium (IGrM). We investigate the projected X-ray emission-weighted abundance profiles of key elements over two decades in halo mass ($10^{13} \leq M_{500}/\mathrm{M_\odot} \leq 10^{15}$). Typically, SIMBA-C generates lower-amplitude abundance profiles than SIMBA with flatter cores, in better agreement with observations. For low-mass groups, both simulations over-enrich the IGrM with Si, S, Ca, and Fe compared to observations, a trend likely related to inadequate modeling of metal dispersal and mixing. We analyze the 3D mass-weighted abundance profiles, concluding that the lower SIMBA-C IGrM abundances are primarily a consequence of fewer metals in the IGrM, driven by reduced metal yields in Chem5, and the removal of the instantaneous recycling of metals approximation employed by SIMBA. Additionally, an increased IGrM mass in low-mass SIMBA-C groups is likely triggered by changes to the AGN and stellar feedback models. Our study suggests that a more realistic chemical enrichment model broadly improves agreement with observations, but physically motivated sub-grid models for other key processes, like AGN and stellar feedback and turbulent diffusion, are required to realistically reproduce observed group environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".