‘Observing’ unrelaxed clusters in dark matter simulations
Bibliographic record
Abstract
ABSTRACT We present a detailed study of relaxed and unrelaxed galaxy clusters in a large dark matter only simulation. Recent work has demonstrated clear differences between the galaxy populations in clusters that have Gaussian velocity distributions (relaxed) compared to those that do not (unrelaxed). To directly compare with observations, we identify unrelaxed clusters in the simulations using one-dimensional velocity distributions. We show that non-Gaussian clusters have had recent major mergers and enhanced rates of galaxy infall relative to systems with Gaussian velocity profiles. Furthermore, we find that the fraction of non-Gaussian clusters increases strongly with cluster mass and modestly with redshift. For comparison, we also make use of three-dimensional information available in the simulations to explore the impact of projection on observational measurements. Differences between Gaussian and non-Gaussian clusters are much stronger when three-dimensional information is considered, which demonstrates that the strength of oberserved trends with cluster dynamics are diluted because observed velocity information is limited to one line-of-sight.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".