<scp>hyenas</scp>: X-ray bubbles and cavities in the intragroup medium
Bibliographic record
Abstract
ABSTRACT We investigate the role of the simba feedback model on the structure of the intragroup medium (IGrM) in the new hyenas suite of cutting-edge cosmological zoom-in simulations. Using 34 high-resolution zooms of haloes spanning from $10^{13}-10^{14}$${\rm M_\odot}$ at $z=0.286$, we follow haloes for 700 Myr, over several major active galactic nuclei (AGNs) jet feedback events. We use the moxha package to generate mock Chandra X-ray observations, as well as predictive mocks for the upcoming LEM mission, identifying many feedback-generated features such as cavities, shock-fronts, and hot-spots, closely mimicking real observations. Our sample comprises 105 snapshots with identified cavities, 50 with single bubbles, and 55 with two, and spans three orders of magnitude in observed cavity enthalpies, from $10^{41}-10^{44}$ erg s−1. Comparing semimajor axis length, midpoint radius, and eccentricity to a matched sample of observations, we find good agreement in cavity dimensions with real catalogues. We estimate cavity power from our mock maps following observational procedures, showing that this is typically more than enough to offset halo cooling, particularly in low-mass haloes, where we match the observed excess in energy relative to cooling. Bubble enthalpy as measured with the usual midpoint pressure typically exceeds the energy released by the most recent jet event, hinting that the mechanical work is done predominantly at a lower pressure against the IGrM. We demonstrate for the first time that X-ray cavities are observable in a modern large-scale simulation suite and discuss the use of realistic cavity mock observations as new halo-scale constraints on feedback models in cosmological simulations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".