A GALACTIC WEIGH-IN: MASS MODELS OF SINGS GALAXIES USING CHEMO-SPECTROPHOTOMETRIC GALACTIC EVOLUTION MODELS
Bibliographic record
Abstract
The baryonic mass-to-light ratio (ϒ ⋆ ) used to perform the photometry-to-mass conversion has a tremendous influence on the measurement of the baryonic content and distribution as well as on the determination of the dark halo parameters. Since numerous clues hint at an inside-out formation process for galaxies, a radius-dependant ϒ ⋆ is needed to physically represent the radially varying stellar population. In this article, we use chemo-spectrophotometric galactic evolution (CSPE) models to determine ϒ ⋆ for a wide range of masses and sizes in the scenario of an inside-out formation process by gas accretion. We apply our method to a SINGS subsample of 10 spiral and dwarf galaxies with photometric coverage ranging from the UV to the mid-IR. The CSPE models prove to be a good tool for weighting the different photometric bands in order to obtain consistent stellar disk masses regardless of the spectral band used. On the other hand, we show that the color index versus ϒ ⋆ relation is an imperfect tool for assigning masses to young stellar populations because of the degeneracy affecting ϒ ⋆ in all bands at low color index. The disks resulting from our analysis are compatible with the maximum disk hypothesis provided that an adequate bulge/disk decomposition is performed and that the correction for the presence of a bar is not neglected since bars disturb the internal disk kinematics. Disk-mass models including ϒ ⋆ as a free parameter as well as models using our physically motivated, radially varying ϒ ⋆ are presented and discussed for each galaxy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".