MaGICC-WDM: the effects of warm dark matter in hydrodynamical simulations of disc galaxy formation
Bibliographic record
Abstract
We study the effect of warm dark matter (WDM) on hydrodynamic simulations of galaxy formation as part of the Making Galaxies in a Cosmological Context (MaGICC) project. We simulate three different galaxies using three WDM candidates of 1, 2 and 5 keV and compare results with pure cold dark matter simulations. WDM slightly reduces star formation and produces less centrally concentrated stellar profiles. These effects are most evident for the 1 keV candidate but almost disappear for |$m_ {\rm \small {wdm}} >2$| keV. All simulations form similar stellar discs independent of WDM particle mass. In particular, the disc scalelength does not change when WDM is considered. The reduced amount of star formation in the case of 1 keV particles is due to the effects of WDM on merging satellites which are on average less concentrated and less gas rich. The altered satellites cause a reduced starburst during mergers because they trigger weaker disc instabilities in the main galaxy. Nevertheless we show that disc galaxy evolution is much more sensitive to stellar feedback than it is to WDM candidate mass. Overall, we find that WDM, especially when restricted to current observational constraints (|$m_ {\rm \small {wdm}} >2$| keV), has a minor impact on disc galaxy formation.
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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 | 0.004 |
| 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 | 0.001 |
| 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".