Simulated Bars May Be Shorter but Are Not Slower Than Those Observed: TNG50 versus MaNGA
Bibliographic record
Abstract
Abstract Galactic bars are prominent dynamical structures within disk galaxies whose size, formation time, strength, and pattern speed influence the dynamical evolution of their hosts’ galaxies. Yet, their formation and evolution in a cosmological context is not well understood, as cosmological simulation studies have been limited by the classic trade-off between simulation volume and resolution. Here we analyze barred disk galaxies in the cosmological magnetohydrodynamical simulation TNG50 and quantitatively compare the distributions of bar size and pattern speed to those from MaNGA observations at z = 0. TNG50 galaxies are selected to match the stellar mass and size distributions of observed galaxies, to account for observational selection effects. We find that the high resolution of TNG50 yields bars with a wide range of pattern speeds (including those with ≥ 40 km s−1 kpc−1) and a mean value of ∼ 36 km s−1 kpc larger than those from observations by only 6 km s−1 kpc−1, in contrast with previous lower-resolution cosmological simulations that produced bars that were too slow. We find, however, that the bars in TNG50 are on average ∼35% shorter than observed, although this discrepancy may partly reflect the remaining inconsistencies in the simulation-data comparison. This leads to higher values of = R corot / R b in TNG50, but points to simulated bars being too short rather than too slow. After repeating the analysis on the lower-resolution run of the same simulation (with the same physical model), we qualitatively reproduce the results obtained in previous studies: this implies that, along with physical model variations, numerical resolution effects may explain the previously found slowness of simulated bars.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".