<scp>H i</scp> asymmetries in spatially resolved <scp>simba</scp> galaxies
Bibliographic record
Abstract
ABSTRACT We present a study of the neutral atomic hydrogen (H i) content of spatially resolved, low-redshift galaxies in the simba cosmological simulations. We create synthetic H i data cubes designed to match observations from the Apertif Medium-Deep H i imaging survey, and follow an observational approach to derive the H i size–mass relation. The H i size–mass relation for s imba is in broad agreement with the observed relation to within 0.1 dex, but simba galaxies are slightly smaller than expected at fixed H i mass. We quantify the H i spectral ($\rm{A_{\rm{flux}}}$) and morphological ($\rm{A_{\rm{mod}}}$) asymmetries of the galaxies and motivate standardizing the relative spatial resolution when comparing $\rm{A_{\rm{mod}}}$ values in a sample that spans several orders of magnitude in H i mass. Galaxies are classified into three categories (isolated, interacted, or merged) based on their dynamical histories over the preceding $\sim$2 Gyr to contextualize disturbances in their H i reservoirs. We determine that the interacted and merged categories have higher mean asymmetries than the isolated category, with a larger separation between the categories’ $\rm{A_{\rm{mod}}}$ distributions than between their $\rm{A_{\rm{flux}}}$ distributions. For the interacted and merged categories, we find an inverse correlation between baryonic mass and $\rm{A_{\rm{mod}}}$ that is not observed between baryonic mass and $\rm{A_{\rm{flux}}}$. These results, coupled with the weak correlation found between $\rm{A_{\rm{flux}}}$ and $\rm{A_{\rm{mod}}}$, highlight the limitations of only using $\rm{A_{\rm{flux}}}$ to infer the H i distributions of spatially unresolved H i detections.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".