WALLABY Pilot Survey: Characterizing Low-rotation Kinematically Modeled Galaxies
Bibliographic record
Abstract
Abstract Many of the tensions in cosmological models of the Universe lie in the low-mass, low-velocity regime. Probing this regime requires a statistically significant sample of galaxies with well-measured kinematics and robustly measured uncertainties. The Widefield ASKAP L -band Legacy All-sky Blind surveY (WALLABY), as a wide-area, untargeted H i survey, is well positioned to construct this sample. As a first step toward this goal, we develop a framework for testing kinematic modeling codes in the low-resolution, low signal-to-noise ratio, low rotation velocity regime. We find that the WALLABY Kinematic Analysis Proto-Pipeline is remarkably successful at modeling these galaxies when compared to other algorithms, but, even in idealized tests, there is a significant fraction of false positives found below inclinations of ≈40°. We further examine the 11 detections with rotation velocities below 50 km s −1 in the WALLABY pilot data releases. We find that those galaxies with inclinations above 40° lie within 1 σ –2 σ of structural scaling relations that require reliable rotation velocity measurements, such as the baryonic Tully–Fisher relation. Moreover, the subset that has consistent kinematic and photometric inclinations tends to lie nearer to the relations than those that have inconsistent inclination measures. This work both demonstrates the challenges faced in low-velocity kinematic modeling and provides a framework for testing modeling codes as well as constructing a large sample of well-measured low-rotation models from untargeted 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".