Spatially resolved star formation and inside-out quenching in the TNG50 simulation and 3D-HST observations
Bibliographic record
Abstract
ABSTRACT We compare the star-forming main sequence (SFMS) of galaxies – both integrated and resolved on 1 kpc scales – between the high-resolution TNG50 simulation of IllustrisTNG and observations from the 3D-HST slitless spectroscopic survey at z ∼ 1. Contrasting integrated star formation rates (SFRs), we find that the slope and normalization of the star-forming main sequence in TNG50 are quantitatively consistent with values derived by fitting observations from 3D-HST with the Prospector Bayesian inference framework. The previous offsets of 0.2–1 dex between observed and simulated main-sequence normalizations are resolved when using the updated masses and SFRs from Prospector. The scatter is generically smaller in TNG50 than in 3D-HST for more massive galaxies with M*> 1010 M⊙, by ∼10–40 per cent, after accounting for observational uncertainties. When comparing resolved star formation, we also find good agreement between TNG50 and 3D-HST: average specific star formation rate (sSFR) radial profiles of galaxies at all masses and radii below, on, and above the SFMS are similar in both normalization and shape. Most noteworthy, massive galaxies with M*> 1010.5 M⊙, which have fallen below the SFMS due to ongoing quenching, exhibit a clear central SFR suppression, in both TNG50 and 3D-HST. In contrast, the original Illustris simulation and a variant TNG run without black hole kinetic wind feedback, do not reproduce the central SFR profile suppression seen in data. In TNG, inside-out quenching is due to the supermassive black hole (SMBH) feedback model operating at low accretion rates.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".