The structural evolution of isolated galaxies at low redshift in the IllustrisTNG simulation
Bibliographic record
Abstract
ABSTRACT We study the structural evolution of isolated star-forming (SF) galaxies in the IllustrisTNG100-1 hydrodynamical simulation, with a focus on investigating the growth of the central core density within 2 kpc (Σ*,2 kpc) in relation to total stellar mass (M*) at z < 0.5. First, we show that several observational trends in the Σ*,2 kpc–M* plane are qualitatively reproduced in IllustrisTNG, including the distributions of active galactic nuclei (AGN), SF galaxies, quiescent galaxies, and radial profiles of stellar age, specific star formation rate (sSFR), and metallicity. We find that galaxies with dense cores evolve parallel to the Σ*,2 kpc–M* relation, while galaxies with diffuse cores evolve along shallower trajectories. We investigate possible drivers of rapid growth in Σ*,2 kpc compared to M*. Both the current sSFR gradient and the black hole (BH) accretion rate are indicators of past core growth, but are not predictors of future core growth. Major mergers (although rare in our sample; ∼10 per cent) cause steeper core growth, except for high-mass ($M_{\rm *}\gtrsim 10^{10} \, {\rm M}_{\odot }$) mergers, which are mostly dry. Disc instabilities, as measured by the fraction of mass with Toomre Q < 2, are not predictive of rapid core growth. Instead, rapid core growth results in more stable discs. The cumulative BH feedback history sets the maximum rate of core growth, preventing rapid growth in high-mass galaxies ($\gtrsim 10^{9.5} \, {\rm M}_{\odot }$). For massive galaxies, the total specific angular momentum of accreting gas is the most important predictor of future core growth. Our results suggest that the angular momentum of accreting gas controls the slope, width, and zero-point evolution of the Σ*,2 kpc–M* relation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".