The Dragonfly Wide Field Survey. II. Accurate Total Luminosities and Colors of Nearby Massive Galaxies and Implications for the Galaxy Stellar-mass Function
Bibliographic record
Abstract
Abstract Stellar-mass estimates of massive galaxies are susceptible to systematic errors in their photometry, due to their extended light profiles. In this study, we use data from the Dragonfly Wide Field Survey to accurately measure the total luminosities and colors of nearby massive galaxies. The low surface brightness limits of the survey (μ g ≈ 31 mag arcsec−2 on a 1′ scale) allow us to implement a method, based on integrating the 1D surface brightness profile, that is minimally dependent on any parameterization. We construct a sample of 1188 massive galaxies with <?CDATA $\mathrm{log}{M}_{* }/{M}_{\odot }\gt 10.75$?> based on the Galaxy Mass and Assembly (GAMA) survey and measure their total luminosities and g − r colors. We then compare our measurements to various established methods applied to imaging from the Sloan Digital Sky Survey (SDSS), focusing on those favored by the GAMA survey. In general, we find that galaxies are brighter in the r band by an average of ∼0.05 mag and bluer in g − r colors by ∼0.06 mag compared to the GAMA measurements. These two differences have opposite effects on the stellar-mass estimates. The total luminosities are larger by 5% but the mass-to-light ratios are lower by ∼10%. The combined effect is that the stellar-mass estimate of massive galaxies decreases by 7%. This, in turn, implies a small change in the number density of massive galaxies: ≤30% at <?CDATA $\mathrm{log}{M}_{* }/{M}_{\odot }\geqslant 11$?> .
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".