Two-dimensional multi-component photometric decomposition of CALIFA galaxies
Bibliographic record
Abstract
We present a two-dimensional multi-component photometric decomposition of 404 galaxies from the Calar Alto Legacy Integral Field Area data release 3 (CALIFA-DR3). They represent all possible galaxies with no clear signs of interaction and not strongly inclined in the final CALIFA data release. Galaxies are modelled in the g, r, and i Sloan Digital Sky Survey (SDSS) images including, when appropriate, a nuclear point source, bulge, bar, and an exponential or broken disc component. We use a human-supervised approach to determine the optimal number of structures to be included in the fit. The dataset, including the photometric parameters of the CALIFA sample, is released together with statistical errors and a visual analysis of the quality of each fit. The analysis of the photometric components reveals a clear segregation of the structural composition of galaxies with stellar mass. At high masses (log (M⋆/M⊙) > 11), the galaxy population is dominated by galaxies modelled with a single Sérsic or a bulge+disc with a bulge-to-total (B/T) luminosity ratio B/T > 0.2. At intermediate masses (9.5 < log (M⋆/M⊙) < 11), galaxies described with bulge+disc but B/T < 0.2 are preponderant, whereas, at the low mass end (log (M⋆/M⊙) < 9.5), the prevailing population is constituted by galaxies modelled with either purediscs or nuclear point sources+discs (i.e., no discernible bulge). We obtain that 57% of the volume corrected sample of disc galaxies in the CALIFA sample host a bar. This bar fraction shows a significant drop with increasing galaxy mass in the range 9.5 < log (M⋆/M⊙) < 11.5. The analyses of the extended multi-component radial profile result in a volume-corrected distribution of 62%, 28%, and 10% for the so-called Type I (pure exponential), Type II (down-bending), and Type III (up-bending) disc profiles, respectively. These fractions are in discordance with previous findings. We argue that the different methodologies used to detect the breaks are the main cause for these differences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".