X-ray shocks in the cool cores of galaxy clusters: insights from TNG-Cluster
Bibliographic record
Abstract
ABSTRACT Shock fronts driven by active galactic nuclei in galaxy cluster cores represent a promising mechanism for heating the intracluster medium and offsetting radiative cooling. Despite their potential importance, they are challenging to detect and have been identified in only about ten massive clusters. We present the first systematic detection and characterization of active galactic nuclei (AGN)-driven shocks in simulated clusters from the TNG-Cluster magnetohydrodynamic cosmological zoom-in simulations of galaxies. TNG-Cluster exhibits a rich variety of spatially resolved X-ray structures, including realistic populations of X-ray cavities, as well as shocks, produced by its AGN feedback model, without collimated, relativistic jets, nor cosmic rays. We produce mock Chandra observations with 600 ks exposures for 100 clusters, mass-matched (M$_{\text{500c}} = 1.2$–$8.5 \times 10^{14}$ M$_\odot$) to the ten observed clusters exhibiting shocks. Using observational techniques, we detect 50 shocks in 30 of the 100 clusters, with $\sim$35 per cent hosting multiple shocks. These shocks typically lie within a hundred kiloparsec of the central supermassive black holes, are weak (Mach $<$ 2, median $\sim$ 1.1), and are associated with X-ray cavities in about half of the cases. Both in observations and in TNG-Cluster, shocks tend to be located at larger radii than cavities, with median offsets of 46 and 27 kpc, respectively. The observationally inferred shock powers are comparable to the cluster cooling luminosities (10$^{44-46}$ erg s$^{-1}$), suggesting that shocks in the simulation are a crucial heating mechanism. Our results indicate that shocks play a role as important as cavities in balancing cooling in cluster cores, acting isotropically and up to larger distances.
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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 | 0.000 |
| 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 | 0.000 | 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".