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 deg2 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 |$10^{11}\,h_{70}^{-2}\,\mathrm{L}_{{\odot }}$| and |$2\times 10^{11}\,h_{70}^{-2}\,\mathrm{M}_{{\odot }}$| 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 |$1.19^{+0.06}_{-0.07}\times 10^{13}\,h_{70}^{-1}\,\mathrm{M}_{{\odot }}$| for red galaxies, while for blue galaxies the best-fitting slope is |$1.09^{+0.20}_{-0.13}$| and the normalization is |$0.18^{+0.04}_{-0.05}\times 10^{13}\,h_{70}^{-1}\,\mathrm{M}_{{\odot }}$|. Similarly, we find a best-fitting slope of |$1.36^{+0.06}_{-0.07}$| and a normalization of |$1.43^{+0.11}_{-0.08}\times 10^{13}\,h_{70}^{-1}\,\mathrm{M}_{{\odot }}$| 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 |$0.84^{+0.20}_{-0.16}\times 10^{13}\,h_{70}^{-1}\,\mathrm{M}_{{\odot }}$|. 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 |$2\times 10^{9}\,h_{70}^{-2}\,\mathrm{M}_{{\odot }}$|. 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 from galaxy–galaxy lensing that blue galaxies reside in less clustered environments than red galaxies. We also find that the halo model, while matching the lensing signal around red lenses well, is prone to overpredicting the large-scale signal for faint and less massive blue lenses. This could be a further indication that these galaxies tend to be more isolated than assumed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".