Bibliographic record
Abstract
ABSTRACT We forward model the difference in stellar age between post-coalescence mergers and a control sample with the same stellar mass, environmental density, and redshift. In particular, we use a pure sample of 445 post-coalescence mergers from the recent visually confirmed post-coalescence merger sample identified by Bickley et al. and find that post-coalescence mergers are on average younger than control galaxies for 10 < log (M⋆/M⊙) < 11. The difference in age from matched controls is up to 1.5 Gyr, highest for lower stellar mass galaxies. We forward model this difference using parametric star formation histories, accounting for the pre-coalescence inspiral phase of enhanced star formation using close pair data, and a final additive burst of star formation at coalescence. We find a best-fitting stellar mass burst fraction of fburst = ΔM⋆/M⋆, merger = 0.18 ± 0.02 for 10 < log (M⋆/M⊙) < 11 galaxies, with no evidence of a trend in stellar mass. The modelled burst fraction is robust to choice of parametric star formation history, as well as differences in burst duration. The result appears consistent with some prior observationally derived values, but is significantly higher than that found in hydrodynamical simulations. Using published luminous infrared galaxy star formation rates, we find a burst duration increasing with stellar mass, from 120 to 250 Myr. A comparison to published cold gas measurements indicates there is enough molecular gas available in very close pairs to fuel the burst. Additionally, given our stellar mass burst estimate, the predicted cold gas fraction remaining after the burst is consistent with observed post-coalescence mergers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".