The redshift evolution of galactic bar pattern speed in TNG50
Bibliographic record
Abstract
In this paper, the redshift evolution of the galactic bar properties, like the bar length, pattern speed, and bar fraction, has been investigated for simulated galaxies at stellar masses of M* > 1010 M⊙ in the cosmological magnetohydrodynamical simulation TNG50. We focus on the redshift evolution of the bar pattern speeds and the fast bar tension. We show that the median value of the pattern speed of the bars increases as the redshift grows. On the other hand, although the median value of the bar length increases with time, the ratio between the corotation radius and the bar radius - namely, the 𝓡 = RCR/Rbar parameter - increases as well. In other words, the corotation radius increases with a higher rate than the bar length. This directly means that galactic bars slow down with time, or equivalently as the redshift declines. We discuss the possible mechanisms that reduce the pattern speeds in TNG50. We demonstrate that while mergers can have a significant impact on a galaxy’s pattern speed, they do not play a crucial role in the overall evolution of mean pattern speed within the redshift range ɀ ≤ 1.0. Furthermore, we show that the 𝓡 parameter does not correlate with the gas fraction. Consequently, the existence of gas in TNG50 does not alleviate the fast bar tension. We show that the mean value of the pattern speed, computed for all the galaxies irrespective of their mass, at ɀ = 1.0 is Ωp = 70.98 ± 2.34 km s−1 kpc−1 and reduces to Ωp = 33.65 ± 1.07 km s−1 kpc−1 at ɀ = 0.0. This is a direct prediction by TNG50 that bars at ɀ = 1.0 rotate faster by a factor of ~2 compared to bars at ɀ = 0.0.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".