Simulating cosmic rays in clusters of galaxies - I. Effects on the Sunyaev-Zel'dovich effect and the X-ray emission
Bibliographic record
Abstract
We performed high-resolution simulations of a sample of 14 galaxy clusters that span a mass range from 5 × 1013 to 2 × 1015h−1 M⊙ to study the effects of cosmic rays (CRs) on thermal cluster observables such as X-ray emission and the Sunyaev–Zel'dovich effect. We analyse the CR effects on the intra-cluster medium while simultaneously taking into account the cluster's dynamical state as well as the mass of the cluster. The modelling of the CR physics includes adiabatic CR transport processes, injection by supernovae and cosmological structure formation shocks, as well as CR thermalization by the Coulomb interaction and catastrophic losses by hadronic interactions. While the relative pressure contained in CRs within the virial radius is of the order of 2 per cent in our non-radiative simulations, their contribution rises to 32 per cent in our simulations with dissipative gas physics including radiative cooling, star formation and supernova feedback. The relative CR pressure rises towards the outer regions due to a combination of the following effects: CR acceleration is more efficient at the peripheral strong accretion shocks compared to weak central flow shocks, adiabatic compression of a composite of CRs and thermal gas disfavours the CR pressure relative to the thermal pressure due to the softer equation of state of CRs and CR loss processes are more important at the dense centres. Interestingly, in the radiative simulations the relative CR pressure reaches high values of the order of equipartition with the thermal gas in each cluster galaxy due to the fast thermal cooling of gas which diminishes the thermal pressure support relative to that in CRs. This also leads to a lower effective adiabatic index of the composite gas that increases the compressibility of the intra-cluster medium. This effect slightly increases the central density, thermal pressure and the gas fraction. While the X-ray luminosity in low-mass cool core clusters is boosted by up to 40 per cent, the integrated Sunyaev–Zel'dovich effect appears to be remarkably robust and the total flux decrement only slightly reduced by typically 2 per cent. The resolved Sunyaev–Zel'dovich maps, however, show a larger variation with an increased central flux decrement.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".