Quenching time-scales in the IllustrisTNG simulation
Bibliographic record
Abstract
ABSTRACT The time-scales for galaxy quenching offer clues to its underlying physical drivers. We investigate central galaxy quenching time-scales in the IllustrisTNG 100-1 simulation, their evolution over time, and the pre-quenching properties of galaxies that predict their quenching time-scales. Defining quenching duration τq as the time between crossing specific star formation rate (sSFR) thresholds, we find that ${\sim} 40{{\ \rm per\ cent}}$ of galaxies quench rapidly with τq < 1 Gyr, but a substantial tail of galaxies can take up to 10 Gyr to quench. Furthermore, 29 per cent of galaxies that left the star-forming main sequence (SFMS) more than 2 Gyr ago never fully quench by z = 0. While the median τq is fairly constant with epoch, the rate of galaxies leaving the SFMS increases steadily over cosmic time, with the rate of slow quenchers being dominant around z ∼ 2–0.7. Compared to fast quenchers (τq < 1 Gyr), slow-quenching galaxies (τq > 1 Gyr) were more massive, had more massive black holes, had larger stellar radii, and accreted gas with higher specific angular momentum (AM) prior to quenching. These properties evolve little by z = 0, except for the accreting gas AM for fast quenchers, which reaches the same high AM as the gas in slow quenchers. By z = 0, slow quenchers also have residual star formation in extended gas rings. Using the expected relationship between stellar age gradient and τq for inside-out quenching we find agreement with Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) integral field unit (IFU) observations. Our results suggest the accreting gas AM and potential well depth determine the quenching time-scale.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".