Revisiting the tension between fast bars and the ΛCDM paradigm
Bibliographic record
Abstract
The pattern speed with which galactic bars rotate is intimately linked to the amount of dark matter in the inner regions of their host galaxies. In particular, dark matter haloes act to slow down bars via torques exerted through dynamical friction. Observational studies of barred galaxies tend to find that bars rotate fast, while hydrodynamical cosmological simulations of galaxy formation and evolution in the Lambda cold dark matter (ΛCDM) framework have previously found that bars slow down excessively. This has led to a growing tension between fast bars and the ΛCDM cosmological paradigm. In this study we revisit this issue, using the Auriga suite of high-resolution, magneto-hydrodynamical cosmological zoom-in simulations of galaxy formation and evolution in the ΛCDM framework, finding that bars remain fast down to z = 0. In Auriga, bars form in galaxies that have higher stellar-to-dark matter ratios and are more baryon-dominated than in previous cosmological simulations; this suggests that in order for bars to remain fast, massive spiral galaxies must lie above the commonly used abundance matching relation. While this reduces the aforementioned tension between the rotation speed of bars and ΛCDM, it accentuates the recently reported discrepancy between the dynamically inferred stellar-to-dark matter ratios of massive spirals and those inferred from abundance matching. Our results highlight the potential of using bar dynamics to constrain models of galaxy formation and evolution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".