Virial masses and the baryon fraction in galaxies
Bibliographic record
Abstract
We have measured the weak lensing signal as a function of restframe luminosity for a sample of `isolated' galaxies. These results are based on four-band photometry from the Red-Sequence Cluster Survey, enabling us to determine photometric redshifts for a large number of galaxies. We select a secure sample of lenses with photometric redshifts 0.2<z<0.4 and study the relation between the virial mass and baryonic contents. In addition, we discuss the implications of the derived photometric redshift distribution for published cosmic shear studies. The virial masses are derived from a fit to the observed lensing signal. For a galaxy with a fiducial luminosity of 10^10 h^-2 L_Bsun we obtain a mass M_vir=9.9^{+1.5}_{-1.3}\\times 10^11 h^-1 M_sun. The virial mass as a function of luminosity is consistent with a power-law ~L^1.5, with similar slopes for the three filters considered here. These findings are in excellent agreement with results from the Sloan Digital Sky Survey and semi-analytic models of galaxy formation. We measure the fraction of mass in stars and the baryon fraction in galaxies by comparing the virial mass-to-light ratio to predicted stellar mass-to-light ratios. We find that star formation is inefficient in converting baryons into stars, with late-type galaxies converting ~33% and early-type galaxies converting only ~14% of baryons into stars. Our results imply that the progenitors of early-type galaxies must have low stellar mass fractions, suggestive of a high formation redshift.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".