ASymba: H <scp>i</scp> global profile asymmetries in the <scp>simba</scp> simulation
Bibliographic record
Abstract
ABSTRACT Asymmetry in the spatially integrated, 1D H i global profiles of galaxies can inform us on both internal (e.g. outflows) and external (e.g. mergers, tidal interactions, and ram pressure stripping) processes that shape galaxy evolution. Understanding which of these primarily drive H i profile asymmetry is of particular interest. In the lead-up to SKA pathfinder and SKA H i emission surveys, hydrodynamical simulations have proved to be a useful resource for such studies. Here, we present the methodology behind, as well as first results, of ASymba: Asymmetries in H i of Simba galaxies, the first time this simulation suite has been used for this type of study. We generate mock observations of the H i content of these galaxies and calculate the profile asymmetries using three different methods. We find that $M_{\rm H\, {\small I}}$ has the strongest correlation with all asymmetry measures, with weaker correlations also found with the number of mergers a galaxy has undergone, and gas and galaxy rotation. We also find good agreement with the xGASS sample, in which galaxies with highly asymmetric profiles tend to have lower H i gas fractions than galaxies with symmetric profiles, and additionally find the same holds in sSFR parameter space. For low H i mass galaxies, it is difficult to distinguish between asymmetric and symmetric galaxies, but this becomes achievable in the high H i mass population. These results showcase the potential of ASymba and provide the groundwork for further studies, including comparison to upcoming large H i emission surveys.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".