On the origin of cores in simulated galaxy clusters
Bibliographic record
Abstract
The diffuse plasma that fills galaxy groups and clusters (the intracluster medium) is a by-product of galaxy formation. The present thermal state of this gas results from a competition between gas cooling and heating. The heating comes from two distinct sources: gravitational heating associated with the collapse of the dark matter halo and additional thermal input from the formation of galaxies and their black holes. A long-term goal of this research is to decode the observed temperature, density and entropy profiles of clusters and to understand the relative roles of these processes. However, a long-standing problem has been that cosmological simulations based on smoothed particle hydrodynamics (SPH) and Eulerian mesh-based codes predict different results even when cooling and galaxy/black hole heating are switched off. Clusters formed in SPH simulations show near power-law entropy profiles, while those formed in Eulerian simulations develop a core and do not allow gas to reach such low entropies. Since the cooling rate is closely connected to the minimum entropy of the gas distribution, the differences are of potentially key importance. In this paper, we investigate the origin of this discrepancy. By comparing simulations run using the GADGET-2 SPH code and the FLASH adaptive Eulerian mesh code, we show that the discrepancy arises during the idealized merger of two clusters and that the differences are not the result of the lower effective resolution of Eulerian cosmological simulations. The difference is not sensitive to the minimum mesh size (in Eulerian codes) or the number of particles used (in SPH codes). We investigate whether the difference is the result of the different gravity solvers, the Galilean non-invariance of the mesh code or an effect of unsuitable artificial viscosity in the SPH code. Instead, we find that the difference is inherent to the treatment of vortices in the two codes. Particles in the SPH simulations retain a close connection to their initial entropy, while this connection is much weaker in the mesh simulations. The origin of this difference lies in the treatment of eddies and fluid instabilities. These are suppressed in the SPH simulations, while the cluster mergers generate strong vortices in the Eulerian simulations that very efficiently mix the fluid and erase the low-entropy gas. We discuss the potentially profound implications of these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".