The impact of early massive mergers on the chemical evolution of Milky Way-like galaxies: insights from NIHAO-UHD simulations
Bibliographic record
Abstract
ABSTRACT Recent observations of the Milky Way (MW) found an unexpected steepening of the star-forming gas metallicity gradient around the time of the Gaia–Sausage–Enceladus (GSE) merger event. Here, we investigate the influence of early (tmerger ≲ 5 Gyr) massive ($M_{\rm {gas}}^{\rm {merger}}/M_{\rm {gas}}^{\rm {main}}(t_{\rm {merger}})\gtrsim 10~{{\ \rm per\ cent}}$) merger events such as the Gaia–Sausage–Enceladus merger in the MW on the evolution of the cold gas metallicity gradient. We use the NIHAO-UHD suite of cosmological hydrodynamical simulations of MW-mass galaxies to study the frequency of massive early mergers and their detailed impact on the morphology and chemistry of the gaseous discs. We find a strong steepening of the metallicity gradient at early times for all four galaxies in our sample which is caused by a sudden increase in the cold gas disc size (up to a factor of 2) in combination with the supply of unenriched gas (∼0.75 dex lower compared to the main galaxy) by the merging dwarf galaxies. The mergers mostly affect the galaxy outskirts and lead to an increase in cold gas surface density of up to 200 per cent outside of ∼8 kpc. The addition of unenriched gas breaks the self-similar enrichment of the inter-stellar-medium and causes a dilution of the cold gas in the outskirts of the galaxies. The accreted stars and the ones formed later out of the accreted gas inhabit distinct tracks offset to lower [α/Fe] and [Fe/H] values compared to the main galaxy’s stars. We find that such mergers can contribute significantly to the formation of a second, low-α sequence as is observed in the MW.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".