Investigating bar structure of disc galaxies via primal: a particle-by-particle M2M algorithm
Bibliographic record
Abstract
We have modified our particle-by-particle adaptation of the made-to-measure (M2M) method, with the aim of modelling the Galactic disc from upcoming Galactic stellar survey data. In our new particle-by-particle M2M algorithm, primal, the observables of the target system are compared with those of the model galaxy at the position of the target stars, i.e. particles. The mass of the model particles are adjusted to reproduce the observables of the target system, and the gravitational potential is automatically adjusted by the changing mass of the particles. This paper builds upon our previous work, introducing likelihood-based velocity constraints in primal. In this paper, we apply primal to barred disc galaxies created by an N-body simulation in a known dark matter potential, with no error in the observables. This paper demonstrates that primal can recover the radial profiles of the surface density, velocity dispersion in the radial and perpendicular directions, and the rotational velocity of the target discs, along with the apparent bar structure and pattern speed of the bar, especially when the reference frame is adjusted so that the bar angle of the target galaxy is aligned to that of the model galaxy at every timestep.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".