Bibliographic record
Abstract
The many unique properties of galaxies are shaped by physical processes that affect different components of the galaxy – such as their bulges and discs – in different ways, and they leave characteristic imprints on the light and spectra of these components. Disentangling these spectra reveals vital clues that can be traced back in time to understand how galaxies, and their components, form and evolve throughout their lifetimes. With BUDDI, we have decomposed the integral field unit (IFU) datacubes in SDSS-MaNGA DR17 into Sérsic bulge and exponential disc components and extracted clean bulge and disc spectra. BUDDI-MaNGA is the first large statistical sample of such decomposed spectra of 1452 galaxies covering morphologies from ellipticals to late-type spirals. We derive stellar masses of the individual components with spectral energy distribution (SED) fitting using BAGPIPES and estimate their mean mass-weighted stellar metallicities and stellar ages using PPXF. With this information, we reconstruct the mass assembly histories of the bulges and discs of 968 spiral galaxies (Sa-Sm types). Our results show a clear downsizing effect especially for the bulges, with more massive components assembling earlier and faster than the less massive ones. Additionally, we compare the stellar populations of the bulges and discs in these galaxies, and find that a majority of the bulges host more metal-rich and older stars than their disc counterparts. Nevertheless, we also find a non-negligible fraction of the spiral galaxy population in our sample contains bulges that are younger and more metal-enhanced than their discs. We interpret these results, taking into account how their formation histories and current stellar populations depend on stellar mass and morphology.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.340 | 0.324 |
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".