On the relationship between gas content, star formation, and global H <scp>i</scp> asymmetry of galaxies on the star-forming main-sequence
Bibliographic record
Abstract
ABSTRACT Observations have revealed that disturbances in the cold neutral atomic hydrogen (H i) in galaxies are ubiquitous, but the reasons for these disturbances remain unclear. While some studies suggest that asymmetries in integrated H i spectra (global H i asymmetry) are higher in H i-rich systems, others claim that they are preferentially found in H i-poor galaxies. In this work, we utilize the Arecibo Legacy Fast ALFA (ALFALFA) and extended GALEX Arecibo SDSS Survey (xGASS) surveys, plus a sample of post-merger galaxies, to clarify the link between global H i asymmetry and the gas properties of galaxies. Focusing on star-forming galaxies in ALFALFA, we find that elevated global H i asymmetry is not associated with a change in the H i content of a galaxy, and that only the galaxies with the highest global H i asymmetry show a small increase in specific star formation rate (sSFR). However, we show that the lack of a trend with H i content is because ALFALFA misses the ‘gas-poor’ tail of the star-forming main-sequence. Using xGASS to obtain a sample of star-forming galaxies that is representative in both sSFR and H i content, we find that global H i asymmetric galaxies are typically more gas-poor than symmetric ones at fixed stellar mass, with no change in sSFR. Our results highlight the complexity of the connection between galaxy properties and global H i asymmetry. This is further confirmed by the fact that even post-merger galaxies show both symmetric and asymmetric H i spectra, demonstrating that merger activity does not always lead to an asymmetric global H i spectrum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".