Inferring the Morphology of Stellar Distribution in TNG50: Twisted and Twisted-stretched Shapes
Bibliographic record
Abstract
Abstract We investigate the morphology of the stellar distribution (SD) in a sample of Milky Way–like galaxies in the TNG50 simulation. Using a local in shell iterative method as the main approach, we explicitly show evidence of twisting (in about 52% of halos) and stretching (in 48% of them) in real space. This is matched with the reorientation observed in the eigenvectors of the inertia tensor and gives us a clear picture of having a reoriented SD. We make a comparison between the shape profile of the dark matter (DM) halo and SD and quite remarkably see that their radial profiles are fairly close, especially at small galactocentric radii, where the stellar disk is located. This implies that the DM halo is somewhat aligned with stars in response to the baryonic potential. The level of alignment mostly decreases away from the center. We study the impact of substructures in the orbital circularity parameter. It is demonstrated that in some cases, faraway substructures are counterrotating compared with the central stars and may flip the sign of total angular momentum and thus the orbital circularity parameter. Truncating them above 150 kpc, however, retains the disky structure of the galaxy as per initial selection. Including the impact of substructures in the shape of stars, we explicitly show that their contribution is subdominant. Overlaying our theoretical results on the observational constraints from previous literature, we establish fair agreement.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".