SDSS-IV MaNGA: full spectroscopic bulge-disc decomposition of MaNGA early-type galaxies
Bibliographic record
Abstract
By applying spectroscopic decomposition methods to a sample of MaNGA early-type galaxies, we separate out spatially and kinematically distinct stellar populations, allowing us to explore the similarities and differences between galaxy bulges and discs, and how they affect the global properties of the galaxy. We find that the components have interesting variations in their stellar populations, and display different kinematics. Bulges tend to be consistently more metal rich than their disc counterparts, and while the ages of both components are comparable, there is an interesting tail of younger, more metal-poor discs. Bulges and discs follow their own distinct kinematic relationships, both on the plane of the stellar spin parameter, λR, and ellipticity, ϵ, and in the relation between stellar mass, M*, and specific angular momentum, j*, with the location of the galaxy as a whole on these planes being determined by how much bulge and disc it contains. As a check of the physical significance of the kinematic decompositions, we also dynamically model the individual galaxy components within the global potential of the galaxy. The resulting components exhibit kinematic parameters consistent with those from the spectroscopic decomposition, and though the dynamical modelling suffers from some degeneracies, the bulges and discs display systematically different intrinsic dynamical properties. This work demonstrates the value in considering the individual components of galaxies rather than treating them as a single entity, which neglects information that may be crucial in understanding where, when, and how galaxies evolve into the systems we see today.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".