Galaxy pairs in the Sloan Digital Sky Survey – X. Does gas content alter star formation rate enhancement in galaxy interactions?
Bibliographic record
Abstract
New spectral line observations, obtained with the Jansky Very Large Array (VLA), of a sample of 34 galaxies in 17 close pairs are presented in this paper. The sample of galaxy pairs is selected to contain galaxies in close, major interactions (i.e. projected separations <30 |$h_{70}^{-1}$| kpc, and mass ratios less extreme than 4:1), while still having a sufficiently large angular separation that the VLA can spatially resolve both galaxies in the pair. Of the 34 galaxies, 17 are detected at >3σ. We compare the H i gas fraction of the galaxies with the triggered star formation present in that galaxy. When compared to the star formation rates (SFRs) of non-pair galaxies matched in mass, redshift, and local environment, we find that the star formation enhancement is weakly positively correlated (∼2.5σ) with H i gas fraction. In order to help understand the physical mechanisms driving this weak correlation, we also present results from a small suite of binary galaxy merger simulations with varying gas fractions. The simulated galaxies indicate that larger initial gas fractions are associated with lower levels of interaction-triggered star formation (relative to an identical galaxy in isolation), but also show that high gas fraction galaxies have higher absolute SFRs prior to an interaction. We show that when interaction-driven SFR enhancements are calculated relative to a galaxy with an average gas fraction for its stellar mass, the relationship between SFR and initial gas fraction dominates over the SFR enhancements driven by the interaction. Simulated galaxy interactions that are matched in stellar mass but not in gas fraction, like our VLA sample, yield the same general positive correlation between SFR enhancement and gas fraction that we observe.
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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 | 0.000 |
| 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".