Introducing<scp>romulusc</scp>: a cosmological simulation of a galaxy cluster with an unprecedented resolution
Bibliographic record
Abstract
We present the first results from RomulusC, the highest resolution cosmological hydrodynamic simulation of a galaxy cluster run to date. RomulusC, a zoom-in simulation of a halo with $z=0$ mass $10^{14}$ M$_{\odot}$, is run with the same sub-grid physics and resolution as Romulus25 (Tremmel et al. 2017). With unprecedented mass and spatial resolution, RomulusC represents a unique opportunity to study the evolution of galaxies in dense environments down to dwarf masses. We demonstrate that RomulusC results in an intracluster medium (ICM) consistent with observations. The star formation history and stellar mass of the brightest cluster galaxy (BCG) is consistent with observations and abundance matching results, indicating that our sub-grid models, optimized only to reproduce observations of field dwarf and Milky Way mass galaxies, are able to produce reasonable galaxy masses and star formation histories in much higher mass systems. Feedback from supermassive black holes (SMBHs) regulates star formation by driving large-scale, collimated outflows that coexist with a low entropy core. We find that non-BCG cluster member galaxies are substantially quenched compared to the field down to dwarf galaxy masses and, at low masses, quenching is seen to have no dependence on mass or distance from the cluster center. This enhanced quenched population extends beyond $R_{200}$ and is in place at high redshift. Similarly, we predict that SMBH activity is significantly suppressed within clusters outside of the BCG, but show how the effect could be lost when only focusing on the brightest AGN in the most massive galaxies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".