An investigation of Sloan Digital Sky Survey imaging data and multiband scaling relations of spiral galaxies
Bibliographic record
Abstract
We have compiled a sample of 3041 spiral galaxies with multiband gri imaging from the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7; Abazajian et al. 2009) and available galaxy rotational velocities, V, derived from H i linewidths. We compare the data products provided through the SDSS imaging pipeline with our own photometry of the SDSS images, and use the velocities, V, as an independent metric to determine ideal galaxy sizes (R) and luminosities (L). Our radial and luminosity parameters improve upon the SDSS DR7 Petrosian radii and luminosities through the use of isophotal fits to the galaxy images. This improvement is gauged via VL and RV relations whose respective scatters are reduced by ∼8 and ∼30 per cent with our parameters compared to similar relations built with SDSS parameters. The tightest VRL relations are obtained with the i-band radius, R23.5, i, measured at 23.5 mag arcsec−2, and the luminosity L23.5, i, measured within R23.5, i. Our VRL scaling relations compare well, both in scatter and slope, with similar studies (though such comparisons depend sensitively on the nature and size of the compared samples). The typical slopes, b, and observed scatters, σ, of the i-band VL, RL and RV relations are bVL = 0.27 ± 0.01, bRL = 0.41 ± 0.01, bRV = 1.52 ± 0.07, and σVL = 0.074, σRL = 0.071, σRV = 0.154, respectively. Similar results for the SDSS g and r bands are also provided. Smaller scatters may be achieved with more pruned samples. We also compute scaling relations in terms of the baryonic mass (stars + gas), Mbar, ranging from Mbar ≃ 108.7 to 1011.6 M⊙. Our baryonic velocity–mass (VM) relation has slope 0.29 ± 0.01 and a measured scatter σmeas = 0.076 dex.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".