Dark MaGICC: the effect of dark energy on disc galaxy formation. Cosmology does matter
Bibliographic record
Abstract
We present the Dark MaGICC (Making Galaxies in a Cosmological Context) project, which aims to investigate the effect of dark energy (DE) modelling on disc galaxy formation via hydrodynamical cosmological simulations. Dark MaGICC includes four dynamical DE scenarios with time varying equations of state, one with a self-interacting Ratra–Peebles model. In each scenario, we simulate three disc galaxies with high resolution using smoothed particle hydrodynamics. The baryonic physics model is the same used in the MaGICC project, and we varied only the background cosmology. We find that the DE parametrization has a surprisingly important impact on galaxy evolution and on structural properties of galaxies at z = 0, in striking contrast with predictions from pure N-body simulations. The different background evolutions can (depending on the behaviour of the DE equation of state) either enhance or quench star formation with respect to a Λ cold dark matter model, at a level similar to the variation of the stellar feedback parametrization, with strong effects on the final galaxy rotation curves. While overall stellar feedback is still the driving force in shaping galaxies, we show that the effect of the DE parametrization plays a larger role than previously thought, especially at lower redshifts. For this reason, the influence of DE parametrization on galaxy formation must be taken into account, especially in the era of precision cosmology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".