IllustrisTNG in the HSC-SSP: image data release and the major role of mini mergers as drivers of asymmetry and star formation
Bibliographic record
Abstract
ABSTRACT At fixed galaxy stellar mass, there is a clear observational connection between structural asymmetry and offset from the star-forming main sequence, ΔSFMS. Herein, we use the TNG50 simulation to investigate the relative roles of major mergers (stellar mass ratios μ ≥ 0.25), minor (0.1 ≤ μ < 0.25), and mini mergers (0.01 ≤ μ < 0.1) in driving this connection amongst star-forming galaxies (SFGs). We use dust radiative transfer post-processing with SKIRT to make a large, public collection of synthetic Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) images of simulated IllustrisTNG (TNG) galaxies over 0.1 ≤ z ≤ 0.7 with log (M⋆/M⊙) ≥ 9 (∼750 k images). Using their instantaneous star formation rates (SFRs), known merger histories/forecasts, and HSC-SSP asymmetries, we show (1) that TNG50 SFGs qualitatively reproduce the observed trend between ΔSFMS and asymmetry and (2) a strikingly similar trend emerges between ΔSFMS and the time-to-coalescence for mini mergers. Controlling for redshift, stellar mass, environment, and gas fraction, we show that individual mini merger events yield small enhancements in SFRs and asymmetries that are sustained on long time-scales (at least ∼3 Gyr after coalescence, on average) – in contrast to major/minor merger remnants which peak at much greater amplitudes but are consistent with controls only ∼1 Gyr after coalescence. Integrating the boosts in SFRs and asymmetries driven by μ ≥ 0.01 mergers since z = 0.7 in TNG50 SFGs, we show that mini mergers are responsible for (i) 55 per cent of all merger-driven star formation and (ii) 70 per cent of merger-driven asymmetric structure. Due to their relative frequency and prolonged boost time-scales, mini mergers dominate over their minor and major counterparts in driving star formation and asymmetry in SFGs.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".