Thin disc, thick disc and halo in a simulated galaxy
Bibliographic record
Abstract
Within a cosmological hydrodynamical simulation, we form a disc galaxy with sub-components which can be assigned to a thin stellar disc, thick disc and a low-mass stellar halo via a chemical decomposition. The thin- and thick-disc populations so selected are distinct in their ages, kinematics and metallicities. Thin-disc stars are young (<6.6 Gyr), possess low velocity dispersion (σU, V, W = 41, 31, 25 km s-1), high [Fe/H] and low [O/Fe]. Conversely, the thick-disc stars are old (6.6 < age < 9.8 Gyr), lag the thin disc by ˜21 km s-1, possess higher velocity dispersion (σU, V, W = 49, 44, 35 km s-1) and have relatively low [Fe/H] and high [O/Fe]. The halo component comprises less than 4 per cent of stars in the 'solar annulus' of the simulation, has low metallicity, a velocity ellipsoid defined by σU, V, W = 62, 46, 45 km s-1 and is formed primarily in situ during an early merger epoch. Gas-rich mergers during this epoch play a major role in fuelling the formation of the old-disc stars (the thick disc). We demonstrate that this is consistent with studies which show that cold accretion is the main source of a disc galaxy's baryons. Our simulation initially forms a relatively short (scalelength ˜1.7 kpc at z = 1) and kinematically hot disc, primarily from gas accreted during the galaxy's merger epoch. Far from being a competing formation scenario, we show that migration is crucial for reconciling the short, hot, discs which form at high redshift in Λ cold dark matter, with the properties of the thick disc at z = 0. The thick disc, as defined by its abundances, maintains its relatively short scalelength at z = 0 (2.31 kpc) compared with the total disc scalelength of 2.73 kpc. The inside-out nature of disc growth is imprinted in the evolution of abundances such that the metal-poor α-young population has a larger scalelength (4.07 kpc) than the more chemically evolved metal-rich α-young population (2.74 kpc).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".