Identifying resonances of the Galactic bar in <i>Gaia</i> DR2: I. Clues from action space
Bibliographic record
Abstract
ABSTRACT Action space synthesizes the orbital information of stars and is well suited to analyse the rich kinematic substructure of the disc in the second Gaia data release's radial velocity sample. We revisit the strong perturbation induced in the Milky Way disc by an m = 2 bar, using test particle simulations and the actions (JR, Lz, Jz) estimated in an axisymmetric potential. These make three useful diagnostics cleanly visible. (1) We use the well-known characteristic flip from outward to inward motion at the outer Lindblad resonance (OLR; l = +1, m = 2), which occurs along the axisymmetric resonance line (ARL) in (Lz, JR), to identify in the Gaia action data three candidates for the bar’s OLR and pattern speed Ωbar: 1.85Ω0, 1.20Ω0, and 1.63Ω0 (with ∼0.1Ω0 systematic uncertainty). The Gaia data is therefore consistent with both slow and fast bar models in the literature, but disagrees with recent measurements of ∼1.45Ω0. (2) For the first time, we demonstrate that bar resonances – especially the OLR – cause a gradient in vertical action 〈Jz〉 with Lz around the ARL via ‘Jz-sorting’ of stars. This could contribute to the observed coupling of 〈vR〉 and 〈|vz|〉 in the Galactic disc. (3) We confirm prior results that the behaviour of resonant orbits is well approximated by scattering and oscillation in (Lz, JR) along a slope ΔJR/ΔLz = l/m centred on the l:m ARL. Overall, we demonstrate that axisymmetrically estimated actions are a powerful diagnostic tool even in non-axisymmetric systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".