A CATALOG OF BULGE+DISK DECOMPOSITIONS AND UPDATED PHOTOMETRY FOR 1.12 MILLION GALAXIES IN THE SLOAN DIGITAL SKY SURVEY
Bibliographic record
Abstract
We perform two-dimensional, point-spread-function-convolved, bulge+disk decompositions in the g and r bandpasses on a sample of 1,123,718 galaxies from the Legacy area of the Sloan Digital Sky Survey Data Release Seven. Four different decomposition procedures are investigated which make improvements to sky background determinations and object deblending over the standard SDSS procedures that lead to more robust structural parameters and integrated galaxy magnitudes and colors, especially in crowded environments. We use a set of science-based quality assurance metrics, namely, the disk luminosity–size relation, the galaxy color–magnitude diagram, and the galaxy central (fiber) colors to show the robustness of our structural parameters. The best procedure utilizes simultaneous, two-bandpass decompositions. Bulge and disk photometric errors remain below 0.1 mag down to bulge and disk magnitudes of g ≃ 19 and r ≃ 18.5. We also use and compare three different galaxy fitting models: a pure Sérsic model, an n b = 4 bulge + disk model, and a Sérsic (free n b ) bulge + disk model. The most appropriate model for a given galaxy is determined by the F -test probability. All three catalogs of measured structural parameters, rest-frame magnitudes, and colors are publicly released here. These catalogs should provide an extensive comparison set for a wide range of observational and theoretical studies of galaxies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.010 | 0.009 |
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".