SDSS-IV MaNGA: characterizing non-axisymmetric motions in galaxy velocity fields using the Radon transform
Bibliographic record
Abstract
We show how the Radon transform (defined as a series of line integrals through an image at different orientations and offsets from the origin) can be used as a simple, non-parametric tool to characterize galaxy velocity fields, specifically their global kinematic position angles (PAk) and any radial variation or asymmetry in PAk. This method is fast and easily automated, making it particularly beneficial in an era where integral field unit (IFU) and interferometric surveys are yielding samples of thousands of galaxies. We demonstrate the Radon transform by applying it to gas and stellar velocity fields from the first ∼2800 galaxies of the SDSS-IV MaNGA IFU survey. We separately classify gas and stellar velocity fields into five categories based on the shape of their radial PAk profiles. At least half of stellar velocity fields and two-thirds of gas velocity fields are found to show detectable deviations from uniform coplanar circular motion, although most of these variations are symmetric about the centre of the galaxy. The behaviour of gas and stellar velocity fields is largely independent, even when PAk profiles for both components are measured over the same radii. We present evidence that one class of symmetric PAk variations is likely associated with bars and/or oval distortions, while another class is more consistent with warped discs. This analysis sets the stage for more in-depth future studies which explore the origin of diverse kinematic behaviour in the galaxy population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".