Using galaxy pairs to probe star formation during major halo mergers
Bibliographic record
Abstract
Currently-proposed galaxy quenching mechanisms predict very different behaviours during major halo mergers, ranging from significant quenching enhancement (e.g. clump-induced gravitational heating models) to significant star formation enhancement (e.g. gas starvation models). To test real galaxies’ behaviour, we present an observational galaxy pair method for selecting galaxies whose host haloes are preferentially undergoing major mergers. Applying the method to central L* (1010 M⊙ < M* < 1010.5 M⊙) galaxies in the Sloan Digital Sky Survey at z < 0.06, we find that major halo mergers can at most modestly reduce the star-forming fraction, from 59 to 47 per cent. Consistent with past research, however, mergers accompany enhanced specific star formation rates for star-formingL* centrals: ∼10 per cent when a paired galaxy is within 200 kpc (approximately the host halo's virial radius), climbing to ∼70 per cent when a paired galaxy is within 30 kpc. No evidence is seen for even extremely close pairs (<30 kpc separation) rejuvenating star formation in quenched galaxies. For galaxy formation models, our results suggest: (1) quenching in L* galaxies likely begins due to decoupling of the galaxy from existing hot and cold gas reservoirs, rather than a lack of available gas or gravitational heating from infalling clumps, (2) state-of-the-art semi-analytic models currently overpredict the effect of major halo mergers on quenching, and (3) major halo mergers can trigger enhanced star formation in non-quenched central galaxies.
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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 | 0.000 |
| 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) | 0.001 | 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".