On the Robustness of the Velocity Anisotropy Parameter in Probing the Stellar Kinematics in Milky Way–Like Galaxies: Takeaway from TNG50 Simulation
Bibliographic record
Abstract
Abstract We analyze the velocity anisotropy of stars in real and energy space for a sample of Milky Way–like galaxies in the TNG50 simulation. We employ different selection criteria, including spatial, kinematic, and metallicity cuts, and make three halo classes ( – ) that show mild-to-strong sensitivity to different selections. The above classes cover 48%, 16%, and 36% of the halos, respectively. We analyze the β radial profiles and divide them into either monotonically increasing radial profiles or ones with peaks and troughs. We demonstrate that halos with monotonically increasing β profiles are mostly from class , while those with peaks/troughs are part of classes and . This means that care must be taken, as the observationally reported peaks/troughs might be a consequence of different selection criteria. We infer the anisotropy parameter β energy space and compare that against the β radial profile. It is seen than 65% of halos with very mild sensitivity to different selections in real space are those for which the β radial and energy profiles are closely related. Consequently, we propose that comparing the β radial and energy profiles might be a novel way to examine the sensitivity to different selection criteria and thus examining the robustness of the anisotropy parameter in tracing stellar kinematics. We compare simulated β radial profiles against various observations and demonstrate that, in most cases, the model diversity is comparable with the error bars from different observations, meaning that the TNG50 models are in good overall agreement with 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".