The observability of galaxy merger signatures in nearby gas-rich spirals
Bibliographic record
Abstract
ABSTRACT Galaxy mergers are crucial to understanding galaxy evolution, therefore we must determine their observational signatures to select them from large IFU galaxy samples such as MUSE and SAMI. We employ 24 high-resolution idealized hydrodynamical galaxy merger simulations based on the ‘Feedback In Realistic Environment’ (FIRE-2) model to determine the observability of mergers to various configurations and stages using synthetic images and velocity maps. Our mergers cover a range of orbital configurations at fixed 1:2.5 stellar mass ratio for two gas rich spirals at low redshift. Morphological and kinematic asymmetries are computed for synthetic images and velocity maps spanning each interaction. We divide the interaction sequence into three: (1) the pair phase; (2) the merging phase; and (3) the post-coalescence phase. We correctly identify mergers between first pericentre passage and 500 Myr after coalescence using kinematic asymmetry with 66 per cent completeness, depending upon merger phase and the field of view of the observation. We detect fewer mergers in the pair phase (40 per cent) and many more in the merging and post-coalescence phases (97 per cent). We find that merger detectability decreases with field of view, except in retrograde mergers, where centrally concentrated asymmetric kinematic features enhances their detectability. Using a cut-off derived from a combination of photometric and kinematic asymmetry, we increase these detections to 89 per cent overall, 79 per cent in pairs, and close to 100 per cent in the merging and post-coalescent phases. By using this combined asymmetry cut-off we mitigate some of the effects caused by smaller fields of view subtended by massively multiplexed integral field spectroscopy programmes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".