Action-Based Dynamical Modelling For The Milky Way Disk
Bibliographic record
Abstract
We present Road Mapping, a full-likelihood dynamical modelling machinery, that aims to recover the Milky Way's (MW) gravitational potential from large samples of stars in the Galactic disk. Road Mapping models the observed positions and velocities of stars with a parameterized, action-based distribution function (DF) in a parameterized axisymmetric gravitational potential (Binney & McMillan 2011, Binney 2012, Bovy & Rix 2013). In anticipation of the Gaia data release in autumn, we have fully tested Road Mapping and demonstrated its robustness against the breakdown of its assumptions. Using large suites of mock data, we investigated in isolated test cases how the modelling would be affected if the data's true potential or DF was not included in the families of potentials and DFs assumed by Road Mapping, or if we misjudged measurement errors or the spatial selection function (SF) (Trick et al., submitted to ApJ). We found that the potential can be robustly recovered — given the limitations of the assumed potential model—, even for minor misjudgments in DF or SF, or for proper motion errors or distances known to within 10%. We were also able to demonstrate that Road Mapping is still successful if the strong assumption of axisymmetric breaks down (Trick et al., in preparation). Data drawn from a highresolution simulation (D'Onghia et al. 2013) of a MW-like galaxy with pronounced spiral arms does neither follow the assumed simple DF, nor does it come from an axisymmetric potential. We found that as long as the survey volume is large enough, Road Mapping gives good average constraints on the galaxy's potential. We are planning to apply Road Mapping to a real data set — the Tycho-2 catalogue (Hog et al. 2000) —very soon, and might be able to present some preliminary results already at the conference.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".