High and low Sérsic index bulges in Milky Way- and M31-like galaxies: origin and connection to the bar with TNG50
Bibliographic record
Abstract
ABSTRACT We study bulge formation in MW/M31-like galaxies in a Λ-cold dark matter scenario, focusing on the origin of high- and low-Sersic index bulges. For this purpose, we use TNG50, a simulation of the IllustrisTNG project that combines a resolution of ∼8 × 104 M⊙ in stellar particles with a cosmological volume 52 cMpc in extent. We parametrize bulge surface brightness profiles by the Sérsic index and the bulge-to-total (B/T) ratio obtained from two-component photometric decompositions. In our sample of 287 MW/M31-like simulated galaxies, $17.1{{\ \rm per\ cent}}$ of photometric bulges exhibit high-Sérsic indices and $82.9{{\ \rm per\ cent}}$ show low-Sérsic indices. We study the impact that the environment, mergers and bars have in shaping the surface brightness profiles. We find no correlation between bulge properties and the environment where they reside. Simulated galaxies with higher Sérsic indices show, on average, a higher fraction of ex situ stars in their kinematically selected bulges. For this bulge population, the last significant merger (total mass ratio msat/mhost > 0.1) occurs, on average, at later times. However, a substantial fraction of low-Sérsic index bulges also experience a late significant merger. We find that bars play an important role in the development of the different types of photometric bulges. The fraction of simulated galaxies with bars is smaller for the high- than for the low-Sérsic index population, reaching differences of $20{{\ \rm per\ cent}}$ at z > 1. Simulated galaxies with high fractions of ex situ stars in the bulge do not develop strong bars. Conversely, simulated galaxies with long-lived strong bars have bulges with ex situ fractions, fex situ < 0.2.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".