Interacting galaxies in the IllustrisTNG simulations -- VIII: Pericentric star formation rate enhancements are driven both by increased fuelling and efficiency
Bibliographic record
Abstract
Using the TNG100-1 cosmological simulations, we explore how galaxy properties, such as specific star formation rate ($\rm sSFR=SFR/M_*$), gas fraction ($\rm f_{gas} \,= \, M_{\rm H}/M_{*}$), and star formation efficiency ($\rm SFE_{H} = SFR/M_{H}$), change over the course of galaxy-galaxy interactions. We identify 18,534 distinct encounters from the reconstructed orbits of a sample of massive galaxies ($\rm M_{*} > 10^{10} \; \rm M_{\odot}$) with companions within a stellar mass ratio of 0.1 to 10. Using these encounters, we study the variation of galaxy properties over time as they approach and move away from pericentric encounters over a redshift range of $0 \leq z < 1$. Following the closest pericentric encounters ($\leq 50$ kpc) of a host galaxy with its companion, we find that sSFR is enhanced by a factor of $1.6 \pm 0.1$ on average within the central stellar half-mass radius (R\textsubscript{1/2}) compared to pre-encounter values. Our results show a time delay between pericentre and maximum sSFR enhancement of $\sim$0.1 Gyr with a mean galaxy separation of 75 kpc. We similarly find that $\rm f_{gas}$ is enhanced by a factor of $1.2 \pm 0.1$, and $\rm SFE_{H}$ is enhanced by a factor of $1.4 \pm 0.1$ following the pericentre of an encounter within the same timescale. Additionally, we find evidence of inflowing gas towards the centre, measured by comparing the $\rm f_{gas}$ and metallicity within the central R\textsubscript{1/2} to the galactic outskirts. We find that approximately 70 per cent of the peak sSFR enhancement can be attributed to the increase in $\rm SFE_{H}$, with the increase in $\rm f_{gas}$ contributing the remaining 30 per cent.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".