WALLABY Pilot Survey and ASymba: Comparing H i Detection Asymmetries to the SIMBA Simulation
Bibliographic record
Abstract
Abstract An avenue for understanding cosmological galaxy formation is to compare morphometric parameters in observations and simulations of galaxy assembly. In this second paper of the ASymba: Asymmetries of H i in SIMBA Galaxies series, we measure atomic gas (H i) asymmetries in spatially resolved detections from the untargeted Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY) survey, and compare them to realizations of WALLABY-like mock samples from the SIMBA cosmological simulations. We develop a Scanline Tracing method to create mock galaxy H i data cubes that minimizes shot noise along the spectral dimension, compared to particle-based methods, and thus minimizes spurious asymmetry contributions. We compute 1D and 3D asymmetries for spatially resolved WALLABY Pilot Survey detections, and we find that the highest 3D asymmetries (A 3D ≳ 0.5) stem from interacting systems or detections with strong bridges or tails. We then construct a series of WALLABY-like mock realizations drawn from the SIMBA 50 Mpc simulation volume and compare their asymmetry distributions. We find that the incidence of high A 3D detections is higher in WALLABY than in the SIMBA mocks, but that difference is not statistically significant (p-value = 0.05). The statistical power of quantitative comparisons of asymmetries such as the one presented here will improve as the WALLABY survey progresses and as simulation volumes and resolutions increase.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".