The galaxy–halo connection from a joint lensing, clustering and abundance analysis in the CFHTLenS/VIPERS field
Bibliographic record
Abstract
We present new constraints on the relationship between galaxies and their host dark matter haloes, measured from the location of the peak of the stellar-to-halo mass ratio (SHMR), up to the most massive galaxy clusters at redshift z ∼ 0.8 and over a volume of nearly 0.1 Gpc3. We use a unique combination of deep observations in the CFHTLenS/VIPERS field from the near-UV to the near-IR, supplemented by ∼60 000 secure spectroscopic redshifts, analysing galaxy clustering, galaxy–galaxy lensing and the stellar mass function. We interpret our measurements within the halo occupation distribution (HOD) framework, separating the contributions from central and satellite galaxies. We find that the SHMR for the central galaxies peaks at |$M_{\rm h, peak} = 1.9^{+0.2}_{-0.1}\times 10^{12}{\,{\rm M}_{{\odot }}}$| with an amplitude of 0.025, which decreases to ∼0.001 for massive haloes (|${{{M}_{\rm h}}}> 10^{14} {\,{\rm M}_{{\odot }}}$|). Compared to central galaxies only, the total SHMR (including satellites) is boosted by a factor of 10 in the high-mass regime (cluster-size haloes), a result consistent with cluster analyses from the literature based on fully independent methods. After properly accounting for differences in modelling, we have compared our results with a large number of results from the literature up to z = 1: we find good general agreement, independently of the method used, within the typical stellar-mass systematic errors at low to intermediate mass (|${{{M}_{\rm \star }}}<10^{11} {\,{\rm M}_{{\odot }}}$|) and the statistical errors above. We have also compared our SHMR results to semi-analytic simulations and found that the SHMR is tilted compared to our measurements in such a way that they over- (under-) predict star formation efficiency in central (satellite) galaxies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".