Automated kinematic modelling of warped galaxy discs in large H i surveys: 3D tilted-ring fitting of H i emission cubes
Bibliographic record
Abstract
Kinematical parametrizations of disc galaxies, employing emission line observations, are indispensable tools for studying the formation and evolution of galaxies. Future large-scale H i surveys will resolve the discs of many thousands of galaxies, allowing a statistical analysis of their disc and halo kinematics, mass distribution and dark matter content. Here, we present an automated procedure which fits tilted-ring models to H i data cubes of individual, well-resolved galaxies. The method builds on the 3D Tilted Ring Fitting Code (TiRiFiC) and is called Fully Automated TiRiFiC (FAT). To assess the accuracy of the code, we apply it to a set of 52 artificial galaxies and 25 real galaxies from the Local Volume H i Survey (LVHIS). Using LVHIS data, we compare our 3D modelling to the 2D modelling methods DiskFit and ROTCUR. A conservative result is that FAT accurately models the kinematics and the morphologies of galaxies with an extent of eight beams across the major axis in the inclination range 20°–90° without the need for priors such as disc inclination. When comparing to 2D methods we find that velocity fields cannot be used to determine inclinations in galaxies that are marginally resolved. We conclude that with the current code tilted-ring models can be produced in a fully automated fashion. This will be essential for future H i surveys, with the Square Kilometre Array and its pathfinders, which will allow us to model the gas kinematics of many thousands of well-resolved galaxies. Performance studies of FAT close to our conservative limits, as well as the introduction of more parametrized models will open up the possibility to study even less resolved galaxies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".