Structure and Kinematics of Star-forming Elliptical Galaxies in SDSS-MaNGA
Bibliographic record
Abstract
Abstract Using “spatially” resolved spectroscopy, we investigated the characteristics and different modes of formation of stars in elliptical galaxies. We identified an unusual population of 59 star-forming elliptical (SF-E) galaxies in SDSS-MaNGA, our primary sample. To identify these rare star-forming ellipticals, we combined GSWLC-A2 containing outputs of stellar population synthesis models with morphological results from the deep-learning catalog and resolved and integrated properties from the MaNGA Pipe3D value-added catalog. We have also constructed two control samples of star-forming spirals (SF-Sps; 2419 galaxies) and quenched ellipticals (Q-Es; 684 galaxies) to compare with our primary sample of SF-Es. Hα emission line flux of SF-Es is similar to spiral galaxies. The D4000 spectral index indicates that SF-Es have a mixture of old and young stellar populations. Mass-weighted stellar age and metallicity for the SF-Es are lower than the Q-Es and 67% of stellar- and gas-velocity maps of the primary sample show signs of kinematic disturbance. All of these indicate that SF-Es have acquired metal-poor gas through recent mergers or interactions with other galaxies and are forming a new generation of stars. Further, we subdivide our primary sample of SF-Es into four classes based on their bulge to total luminosity ratio (B/T) and spin parameter λ re . These four classes have their distinct evolutionary history and modes of formation. Based on these results, we suggest that the Hubble diagram does not accurately capture galaxy evolution processes, and we need a revised morphology diagram like the comb morphology diagram to get a complete picture of the galaxy evolution processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".