Interacting galaxies in the IllustrisTNG simulations – VI: Reconstructed orbits, close encounters, and mergers
Bibliographic record
Abstract
ABSTRACT Cosmological simulations have been used to study interacting galaxies as a function of galaxy pair separation, enabling comparisons with observational studies of galaxy pairs. The study of interacting galaxies as a function of time (i.e. merger stage) has mostly been limited to high-resolution merger simulations, due to the poor time sampling available in cosmological simulations. Building on an earlier study of galaxy pairs in the IllustrisTNG cosmological simulations, we reconstruct the orbits of galaxy pairs involving massive galaxies (M* > 1010 M⊙) at redshifts of 0 ≤ z < 1, using a novel kinematic interpolation scheme to model the orbits in between the IllustrisTNG snapshots (which are separated by 162 Myr, on average). We assess the accuracy of these interpolations using a pre-existing suite of merger simulations, and find that kinematic interpolations provide a remarkable improvement in accuracy compared with interpolations that use only radial separations or 3D positions. We find that nearly 90 per cent of the closest pairs (r < 25 kpc) have had a pericentre encounter within the past Gyr. Many of these close pairs are found on rapidly shrinking orbits, and roughly 85 per cent of these pairs will merge within 1 Gyr. However, approximately 3 per cent of these close pairs appear to be flyby systems that will never merge. These reconstructed orbits will be used in future studies to investigate how and when galaxy properties change during close encounters and mergers between galaxies in IllustrisTNG.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".