The neutral gas content of post-merger galaxies
Bibliographic record
Abstract
Measurements of the neutral hydrogen gas content of a sample of 93 post-merger galaxies are presented, from a combination of matches to the ALFALFA.40 data release and new Arecibo observations. By imposing completeness thresholds identical to that of the ALFALFA (Arecibo Legacy Fast ALFA) survey, and by compiling a mass-, redshift- and environment-matched control sample from the public ALFALFA.40 data release, we calculate gas fraction offsets (Δfgas) for the post-mergers, relative to the control sample. We find that the post-mergers have H i gas fractions that are consistent with undisturbed galaxies. However, due to the relative gas richness of the ALFALFA.40 sample, from which we draw our control sample, our measurements of gas fraction enhancements are likely to be conservative lower limits. Combined with comparable gas fraction measurements by Fertig et al. in a sample of galaxy pairs, who also determine gas fraction offsets consistent with zero, we conclude that there is no evidence for significant neutral gas consumption throughout the merger sequence. From a suite of 75 binary merger simulations we confirm that star formation is expected to decrease the post-merger gas fraction by only 0.06 dex, even several Gyr after the merger. Moreover, in addition to the lack of evidence for gas consumption from gas fraction offsets, the observed H i detection fraction in the complete sample of post-mergers is twice as high as the controls, which suggests that the post-merger gas fractions may actually be enhanced. We demonstrate that a gas fraction enhancement in post-mergers, relative to a stellar mass-matched control sample, would indeed be the natural result of merging randomly drawn pairs from a parent population which exhibits a declining gas fraction with increasing stellar mass.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".