The Implications of Local Fluctuations in the Galactic Midplane for Dynamical Analysis in the Gaia Era
Bibliographic record
Abstract
Abstract Orbital properties of stars, computed from their six-dimensional phase-space measurements and an assumed Galactic potential, are used to understand the structure and evolution of the Galaxy. Stellar actions, computed from orbits, have the attractive quality of being invariant under certain assumptions and are therefore used as quantitative labels of a star’s orbit. We report a subtle but important systematic error that is induced in the actions as a consequence of local midplane variations expected for the Milky Way. This error is difficult to model because it is non-Gaussian and bimodal, with neither mode peaking on the null value. An offset in the vertical position of the Galactic midplane of ∼15 pc for a thin disk-like orbit or ∼120 pc for a thick disk-like orbit induces a 25% systematic error in the vertical action J z . In Feedback in Realistic Environments simulations of Milky Way-mass galaxies, these variations are on the order of ∼100 pc at the solar circle. From observations of the mean vertical velocity variation of ∼5–10 km s −1 with radius, we estimate that the Milky Way midplane variations are ∼60–170 pc, consistent with three-dimensional dust maps. Action calculations and orbit integrations, which assume the global and local midplanes are identical, are likely to include this induced error, depending on the volume considered. Variation in the local standard of rest or distance to the Galactic center causes similar issues. The variation of the midplane must be taken into account when performing dynamical analysis across the large regions of the disk accessible to Gaia and future missions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".