The Strength of Bisymmetric Modes in SDSS-IV/MaNGA Barred Galaxy Kinematics
Bibliographic record
Abstract
Abstract The Sloan Digital Sky Survey-IV/Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Survey data provide an unprecedented opportunity to study the internal motions of galaxies and, in particular, represent the largest sample of barred galaxy kinematic maps obtained to date. We present results from Nirvana, our nonaxisymmetric kinematic modeling code built with a physically motivated Bayesian forward modeling approach, which decomposes MaNGA velocity fields into first- and second-order radial and tangential rotational modes in a generalized and minimally supervised fashion. We use Nirvana to produce models and rotation curves for 1263 unique barred MaNGA galaxies and a matched unbarred control sample. We present our modeling approach, tests of its efficacy, and validation against existing visual bar classifications. Nirvana finds elevated noncircular motions in galaxies identified as bars in imaging, and bar position angles that agree well with visual measurements. The Nirvana-MaNGA barred and control samples provide a new opportunity for studying the influence of nonaxisymmetric internal disk kinematics in a large statistical sample.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".