Interacting galaxies in the IllustrisTNG simulations – II: star formation in the post-merger stage
Bibliographic record
Abstract
ABSTRACT Galaxy mergers are a major evolutionary transformation whose effects are borne out by a plethora of observations and numerical simulations. However, most previous simulations have used idealized, isolated, binary mergers and there has not been significant progress on studying statistical samples of galaxy mergers in large cosmological simulations. We present a sample of 27 691 post-merger (PM) galaxies (0c ≤ z ≤ 1) identified from IllustrisTNG: a cosmological, large box, magnetohydrodynamical simulation suite. The PM sample spans a wide range of merger and galaxy properties (M⋆, μ, fgas). We demonstrate that star-forming (SF) PMs exhibit enhanced star formation rates (SFRs) on average by a factor of ∼2, while the passive PMs show no statistical enhancement. We find that the SFR enhancements: (1) show no dependence on redshift, (2) anticorrelate with the PM’s stellar mass, and (3) correlate with the gas fraction of the PM’s progenitors. However, SF PMs show stronger enhancements which may indicate other processes being at play (e.g. gas phase, feedback efficiency). Although the SFR enhancement correlates mildly with the merger mass ratio, the more abundant minor mergers (0.1 ≤ μ < 0.3) still contribute ${\sim}50{{\ \rm per\ cent}}$ of the total SFR enhancement. By tracing the PM sample forward in time, we find that galaxy mergers can drive significant SFR enhancements which decay over ∼0.5 Gyr independent of the merger mass ratio, although the decay time-scale is dependent on the simulation resolution. The strongest merger-driven starburst galaxies evolve to be passive/quenched on faster time-scales than their controls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".