X-ray cavities in TNG-Cluster: a direct comparison to observations
Bibliographic record
Abstract
ABSTRACT The TNG-Cluster magnetohydrodynamic cosmological simulations produce a diverse population of X-ray cavities in the intracluster medium (ICM) of simulated galaxy clusters. These arise from episodic, high velocity, kinetic energy injections from the central active supermassive black hole (AGN, SMBH). Here, we present the first comprehensive comparative analysis of X-ray cavities in TNG-Cluster with observational data. First, we select a volume-limited sample of 35 real clusters ($z \le 0.071$, $M_\text{500c} = 10^{14\!-\!14.8} \, {\rm M}_\odot$) observed with the Chandra X-ray Observatory, identify three analogues for each in TNG-Cluster (total of 105), and generate mock Chandra images using same exposure times as their observed counterparts. We identify X-ray cavities and measure their properties in both data sets using identical techniques, ensuring a direct, apples-to-apples comparison. Our analysis reveals that both samples have a similar fraction of X-ray cavities (35–43 per cent). They exhibit comparable sizes and morphologies, although the sizes of simulated X-ray cavities still attached to the SMBH are somewhat larger in TNG-Cluster a scarcity at ${<} 10$ kpc. The area of TNG X-ray cavities increases as they rise in the ICM, consistent with the trend of the observational sample. The cavity powers, estimated using observational techniques, show good agreement between the two samples ($10^{42\!-\!45}$ erg s−1), suggesting that X-ray cavities in the simulation are an important heating mechanism in cluster cores. Overall, the rather simple AGN feedback model of TNG, with no model choices made to reproduce X-ray morphological features, and without cosmic rays, creates a quantitatively realistic population of X-ray cavities at cluster scales.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".