MaNGA galaxy properties – I. An extensive optical, mid-infrared photometric, and environmental catalogue
Bibliographic record
Abstract
ABSTRACT We present an extensive catalogue of non-parametric structural properties derived from optical and mid-infrared imaging for 4585 galaxies from the MaNGA survey. DESI and Wide-field Infrared Survey Explorer (WISE) imaging are used to extract surface brightness profiles in the g, r, z, W1, W2 photometric bands. Our optical photometry takes advantage of the automated algorithm autoprof and probes surface brightnesses that typically reach below 29 mag arcsec−2 in the r-band, while our WISE photometry achieves 28 mag arcsec−2 in the W1-band. Neighbour density measures and central/satellite classifications are also provided for a large subsample of the MaNGA galaxies. Highlights of our analysis of galaxy light profiles include (i) an extensive comparison of galaxian structural properties that illustrates the robustness of non-parametric extraction of light profiles over parametric methods; (ii) the ubiquity of bimodal structural properties, suggesting the existence of galaxy families in multiple dimensions; and (iii) an appreciation that structural properties measured relative to total light, regardless of the fractional levels, are uncertain. We study galaxy scaling relations based on photometric parameters, and present detailed comparisons with literature and theory. Salient features of this analysis include the near-constancy of the slope and scatter of the size–luminosity and size–stellar mass relations for late-type galaxies with wavelength, and the saturation of central surface density, measured within 1 kpc, for elliptical galaxies with $M_* \gt 10.7\, {\rm M}_{\odot }$ (corresponding to $\Sigma _1 \simeq 10^{10}\, {\rm M}_{\odot }\, {\rm kpc}^{-2}$). The multiband photometry, environmental parameters, and structural scaling relations presented are useful constraints for stellar population and galaxy formation models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".