The chemical and spatial variations of the bulge’s velocity ellipsoids
Bibliographic record
Abstract
ABSTRACT We study the velocity ellipsoids in an N-body$+$SPH (smooth particle hydrodynamics) simulation of a barred galaxy which forms a bar with a BP bulge. We focus on the 2D kinematics, and quantify the velocity ellipses by the anisotropy, $\beta _{ij}$, the correlation, $\rho _{ij}$, and the vertex deviation, $l_{\rm v}$. We explore the variations in these quantities based on stellar age within the bulge and compare these results with the Milky Way’s bulge using data from APOGEE (Apache Point Observatory Galactic Evolution Experiment) DR16 and Gaia DR3. We first explore the variation of the model’s velocity ellipses in galactocentric velocities, $v_R$ and $v_\phi$, for two bulge populations, a (relatively) young one and an old one. The bar imprints quadrupoles on the distribution of ellipse properties, which are stronger in the young population, as expected from their stronger bar. The quadrupoles are distorted if we use heliocentric velocities $v_r$ and $v_l$. We then project these kinematics along the line of sight onto the $(l,b)$-plane. Along the minor axis $\beta _{rl}$ changes from positive at low $|b|$ to negative at large $|b|$, crossing over at lower $|b|$ in the young stars. Consequently, the vertex deviation peaks at lower $|b|$ in the young population, but reaches similar peak values in the old. The $\rho _{rl}$ is much stronger in the young stars, and traces the bar strength. The APOGEE stars split by the median $\rm [Fe/H]$ follow the same trends. Lastly, we explore the velocity ellipses across the entire bulge region in $(l,b)$ space, finding good qualitative agreement between the model and observations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".