Measuring galaxy asymmetries in 3D
Bibliographic record
Abstract
ABSTRACT One of the commonly used non-parametric morphometric statistics for galaxy profiles and images is the asymmetry statistic. With an eye to current and upcoming large neutral hydrogen (H i) surveys, we develop a 3D version of the asymmetry statistic that can be applied to datacubes. This statistic is more resilient to variations due to the observed geometry than 1D asymmetry measures, and can be successfully applied to lower spatial resolutions (3 – 4 beams across the galaxy major axis) than the 2D statistic. We have also modified the asymmetry definition from an ‘absolute difference’ version to a ‘squared difference’ version that removes much of the bias due to noise contributions for low signal-to-noise observations. Using a suite of mock asymmetric cubes we show that the background-corrected, squared difference 3D asymmetry statistic can be applied to many marginally resolved galaxies in large wide-area H i surveys such as WALLABY on the Australian SKA Pathfinder (ASKAP).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".