On the formation of massive quiescent galaxies with diverse morphologies in the TNG50 simulation
Bibliographic record
Abstract
ABSTRACT Observations have shown that the star formation activity and the morphology of galaxies are closely related but the underlying physical connection is not well understood. Using the TNG50 simulation, we explore the quenching and the morphological evolution of the 102 massive quiescent galaxies in the mass range of 10.5 < log (Mstellar/M⊙) < 11.5 selected at z = 0. We show that galaxies tend to be quenched more rapidly if they (i) are satellites in massive haloes, (ii) have lower star-forming gas fractions, or (iii) inject a larger amount of black hole kinetic feedback energy. Following global evolutionary pathways, we conclude that quiescent discs are mainly disc galaxies that are recently and slowly quenched. Approximately half of the quiescent ellipticals at z = 0 are rapidly quenched at higher redshifts while still disc-like. While quiescent, these gradually become more elliptical mostly by disc heating, yet these ellipticals still retain some degree of rotation. The other half of quiescent ellipticals with the most random motion-dominated kinematics build up large spheroidal components before quenching primarily by mergers, or in some cases, misaligned gas accretion. However, the mergers that contribute to morphological transformation do not immediately quench galaxies in many cases. In summary, we find that quenching and morphological transformation are largely decoupled. We conclude that the TNG black hole feedback – in combination with the stochastic merger history of galaxies – leads to a large diversity of quenching time-scales and a rich morphological landscape.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".