PolCat: Modelling submillimetre polarization of molecular cloud cores using successive parametrized coordinate transformations
Bibliographic record
Abstract
We introduce a software package called polcat for modelling magnetized molecular cloud cores using submillimetre linear polarization and continuum intensity maps from thermal dust emission. Our polcat modelling software builds a three-dimensional triaxial core model via the use of consecutive parametrized coordinate transformations, and produces simulated polarization maps to fit to observational data sets. We utilize a multi-objective evolutionary optimizer to search the parameter space to simultaneously minimize χ2 for the intensity and polarization position angle maps. The aim of this paper is to test polcat by applying it to several artificial data sets, characterizing the capabilities and performance of the code using approximately 400 test runs. We find that polcat is able to distinguish between polarization maps of twisted and non-twisted field geometries and identify the symmetry of the twist when one exists in the data. polcat generally obtains the correct shapes of cores when fitted to models with the correct field geometry. We characterized the degeneracy of our models due to orientation, finding that there are at least eight degenerate core orientations that produce identical polarization maps for the case of triaxial cores. The degeneracy increases with core symmetry. We expect polcat to be a useful tool for modelling observational polarization data sets. Our tests demonstrate that the code can often eliminate incorrect field configurations, while finding a range or potential models that can explain the data. Physical considerations can often further reduce the set of allowed models, resulting in reasonable constraints on field geometry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".