CFHTLenS: the relation between galaxy dark matter haloes and baryons from weak gravitational lensing
Bibliographic record
Abstract
We present a study of the relation between dark matter halo mass and the baryonic content of their host galaxies, quantified through galaxy luminosity and stellar mass. Our investigation uses 154 deg 2 of Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS) lensing and photometric data, obtained from the CFHT Legacy Survey. To interpret the weak lensing signal around our galaxies, we employ a galaxy-galaxy lensing halo model which allows us to constrain the halo mass and the satellite fraction. Our analysis is limited to lenses at redshifts between 0.2 and 0.4, split into a red and a blue sample. We express the relationship between dark matter halo mass and baryonic observable as a power law with pivot points of 10 11 h -2 70 L and 2 10 11 h -2 70 M for luminosity and stellar mass, respectively. For the luminosity-halo mass relation, we find a slope of 1.32 0.06 and a normalization of 1.19 +0.06 -0.07 10 13 h -1 70 M for red galaxies, while for blue galaxies the best-fitting slope is 1.09 +0.20 -0.13 and the normalization is 0.18 +0.04 -0.05 10 13 h -1 70 M . Similarly, we find a best-fitting slope of 1.36 +0.06 -0.07 and a normalization of 1.43 +0.11 -0.08 10 13 h -1 70 M for the stellar mass-halo mass relation of red galaxies, while for blue galaxies the corresponding values are 0.98 +0.08 -0.07 and 0.84 +0.20 -0.16 10 13 h -1 70 M . All numbers convey the 68 per cent confidence limit. For red lenses, the fraction which are satellites inside a larger halo tends to decrease with luminosity and stellar mass, with the sample being nearly all satellites for a stellar mass of 2 10 9 h -2 70 M . The satellite fractions are generally close to zero for blue lenses, irrespective of luminosity or stellar mass. This, together with the shallower relation between halo mass and baryonic tracer, is a direct confirmation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".