The history of stellar metallicity in a simulated disc galaxy
Bibliographic record
Abstract
We explore the chemical distribution of stars in a simulated galaxy. Using simulations of the same initial conditions but with two different feedback schemes (McMaster Unbiased Galaxy Simulations – MUGS – and Making Galaxies in a Cosmological Context – MaGICC), we examine the features of the age–metallicity relation (AMR), and the three-dimensional age–[Fe/H]–[O/Fe] distribution, both for the galaxy as a whole and decomposed into disc, bulge, halo and satellites. The MUGS simulation, which uses traditional supernova feedback, is replete with chemical substructure. This substructure is absent from the MaGICC simulation, which includes early feedback from stellar winds, a modified initial mass function and more efficient feedback. The reduced amount of substructure is due to the almost complete lack of satellites in MaGICC. We identify a significant separation between the bulge and disc AMRs, where the bulge is considerably more metal-rich with a smaller spread in metallicity at any given time than the disc. Our results suggest, however, that identifying the substructure in observations will require exquisite age resolution, of the order of 0.25 Gyr. Certain satellites show exotic features in the AMR, even forming a ‘sawtooth’ shape of increasing metallicity followed by sharp declines which correspond to pericentric passages. This fact, along with the large spread in stellar age at a given metallicity, compromises the use of metallicity as an age indicator, although alpha abundance provides a more robust clock at early times. This may also impact algorithms that are used to reconstruct star formation histories from resolved stellar populations, which frequently assume a monotonically increasing AMR.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".