Hyper Suprime-Cam view of the CMASS galaxy sample
Bibliographic record
Abstract
Aims . We wish to determine the distribution of dark matter halo masses as a function of the stellar mass and the stellar mass profile for massive galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS) constant-mass (CMASS) sample. Methods . We used g r i z y photometry from the Hyper Suprime-Cam (HSC) to obtain Sérsic fits and stellar masses of CMASS galaxies for which HSC weak-lensing data are available. This sample was visually selected to have spheroidal morphology. We applied a cut in stellar mass, log M * / M ⊙ > 11.0, and selected ∼10 000 objects thus. Using a Bayesian hierarchical inference method, we first investigated the distribution of Sérsic index and size as a function of stellar mass. Then, making use of shear measurements from HSC, we measured the distribution of halo mass as a function of stellar mass, size, and Sérsic index. Results . Our data reveal a steep stellar mass-size relation R e ∝ M * β R , with β R larger than unity, and a positive correlation between Sérsic index and stellar mass: n ∝ M * 0.46 . The halo mass scales approximately with the 1.7 power of the stellar mass. We do not find evidence for an additional dependence of halo mass on size or Sérsic index at fixed stellar mass. Conclusions . Our results disfavour galaxy evolution models that predict significant differences in the size growth efficiency of galaxies living in low- and high-mass halos.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".