Interacting galaxies in the IllustrisTNG simulations – V. Comparing the influence of star-forming versus passive companions
Bibliographic record
Abstract
ABSTRACT We study interacting galaxy pairs in the TNG100-1 and TNG300-1 cosmological simulations using previously generated closest companion samples. We study the specific star-formation rates (sSFRs) of massive ($10^{10} \, {\rm M}_{\odot } \lt M_* \lt 10^{12} \, {\rm M}_{\odot }$ ) galaxies at z ≤ 0.2 as a function of separation from the closest companion galaxy. We split our sample based on whether the companion galaxy is star-forming or passive. We find that galaxies with close star-forming companions have sSFRs that are enhanced (on average) by a factor of 2.9 ± 0.3 in TNG100-1 and 2.27 ± 0.06 in TNG300-1 compared to controls, with enhancements present out to separations of ∼300 kpc. Galaxies with passive companions in TNG300-1 exhibit mild sSFR suppression (∼12 per cent) at 100–300 kpc and small sSFR enhancements at separations below 50 kpc. sSFR suppression is strongest in pairs where the galaxy’s stellar mass is more than 2 times that of its passive companion. By generating a stellar mass-matched (‘twinned’) sample in TNG300-1, we show that differences in sSFR trends between companion types are not a result of intrinsic stellar mass differences in star-forming versus passive galaxies. We compare with an analogous sample of galaxy pairs from SDSS, finding consistent results between observations and simulations. Overall, we find that star-forming galaxies show enhanced sSFRs regardless of companion type, but that galaxies with close passive companions are more likely to be passive themselves.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".