The spatially resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
Bibliographic record
Abstract
Context. Understanding the origin of stars within a galaxy, namely whether they formed in situ or were accreted from other galaxies (ex situ), is key to constraining its evolution. When they are spatially resolved, these components provide crucial insights into the mass assembly history of a galaxy. Aims. We predict the spatial distribution of the ex situ stellar mass fraction in MaNGA galaxies and identify distinct assembly histories based on the radial gradients of these predictions in the central regions. Methods. We employed a diffusion model trained on mock MaNGA analogs (MaNGIA) that were derived from the cosmological simulation TNG50. The model learned to predict the posterior distribution of resolved ex situ stellar mass fraction maps that were conditioned on the stellar mass density, the velocity, and the velocity dispersion gradient maps. After validating the model on an unseen test set from MaNGIA, we applied it to MaNGA galaxies to infer the spatially resolved distribution of their ex situ stellar mass fractions, that is, on the fraction of stellar mass in each spaxel originating from mergers. Results. We identified four broad categories of ex situ mass distributions: (1) flat gradient, in situ dominated; (2) flat gradient, ex situ dominated; (3) positive gradient; and (4) negative gradient. The vast majority of MaNGA galaxies fall in the first category. They have flat gradients with low ex situ fractions. This confirms that in situ star formation is the main assembly driver for low- to intermediate-mass galaxies. At high stellar masses ( > 10 11 M ⊙ ), the ex situ maps are more diverse. This highlights the key role of mergers in building the most massive systems. Ex situ mass distributions correlate with the morphology, the star formation activity, the stellar kinematics, and the environment. This indicates that the accretion history is a primary factor in shaping massive galaxies. Finally, by tracing their assembly histories in TNG50, we linked each class to distinct merger scenarios that ranged from secular evolution to merger-dominated growth. Conclusions. The central gradients of the ex situ stellar mass fraction encode meaningful information about the assembly history of galaxies. Our results highlight the power of combining cosmological simulations with machine-learning to infer the unseen components of galaxies from observable properties.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".