NIHAO XVI: the properties and evolution of kinematically selected discs, bulges, and stellar haloes
Bibliographic record
Abstract
We use 25 simulated galaxies from the NIHAO project to define and characterize a variety of kinematic stellar structures: Thin and thick discs, large-scale single discs, classical and pseudobulges, spheroids, inner discs, and stellar haloes. These structures have masses, spins, shapes, and rotational support in good agreement with theoretical expectations and observational data. Above a dark matter halo mass of 2.5 × 1011Mθ, all galaxies have a classical bulge and 70 per cent have a thin and thick disc. The kinematic (thin) discs follow a power-law relation between angular momentum and stellarmass J= 3.4M1.26±0.06, in very good agreement with the prediction based on the empirical stellar-to-halo-mass relation in the same mass range, and show a strong correlation between maximum 'observed' rotation velocity and dark matter halo circular velocity vc = 6.4v0.64±0.04 max . Tracing back in time these structures' progenitors, we find all of them to lose a fraction 1 - fj of their maximum angular momentum. Thin discs are significantly better at retaining their high-redshift spins (fj ∼ 0.70) than thick ones (fj ∼ 0.40). Stellar haloes have their progenitor baryons assembled the latest (z1/2 ∼ 1.1) and over the longest time-scales (τ ∼ 6.2Gyr), and have the smallest fraction of stars born in situ (fin situ = 0.35 ± 0.14). All other structures have 1.5 ≲ z1/2 ≲ 3, τ = 4 ± 2Gyr, and fin situ ≳ 0.9.
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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 | 0.001 |
| 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 | 0.000 |
| 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".