Simulations of cosmic-ray feedback by active galactic nuclei in galaxy clusters
Bibliographic record
Abstract
Feedback processes by active galactic nuclei (AGN) appear to be a key for understanding the nature of the very X-ray luminous cool cores found in many clusters of galaxies. We investigate a numerical model for AGN feedback where for the first time a relativistic particle population in AGN-inflated bubbles is followed within a full cosmological context. In our high-resolution simulations of galaxy cluster formation, we assume that black hole accretion is accompanied by energy feedback that occurs in two different modes, depending on the accretion rate itself. At high accretion rates, a small fraction of the radiated energy is coupled thermally to the gas surrounding the quasar, while in a low-accretion state, mechanically efficient feedback in the form of hot, buoyant bubbles that are inflated by radio activity is considered. Unlike previous work, we inject a non-thermal particle population of relativistic protons into the AGN bubbles, instead of adopting a purely thermal heating. We then follow the subsequent evolution of the cosmic-ray (CR) plasma inside the bubbles, considering both its hydrodynamical interactions and dissipation processes relevant to the CR population. This permits us to analyse the impact of CR bubbles on the surrounding intracluster medium, and in particular, how this contrasts with the purely thermal case. Due to the different buoyancy of relativistic plasma and the comparatively long CR dissipation time-scale, we find substantial changes in the evolution of clusters as a result of CR feedback. In particular, the non-thermal population can provide significant pressure support in central cluster regions at low thermal temperatures, providing a natural explanation for the decreasing temperature profiles found in cool core clusters. At the same time, the morphologies of the bubbles and of the induced X-ray cavities show a striking similarity to observational findings. AGN feedback with CRs also proves efficient in regulating cluster cooling flows so that the total baryon fraction in stars becomes limited to realistic values of the order of ∼10 per cent, more than a factor of 3 reduction compared with cosmological simulations that only consider radiative cooling and supernova feedback. We find that the partial CR support of the intracluster gas also affects the expected signal of the thermal Sunyaev–Zel'dovich effect, with typical modifications of the integrated Compton-y parameter within the virial radius of the order of ∼10 per cent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".