Comparison of the excess mass around CFHTLenS galaxy-pairs to predictions from a semi-analytic model using galaxy-galaxy-galaxy lensing
Bibliographic record
Abstract
\nThe matter environment of galaxies is connected to the physics of galaxy formation and evolution. In particular, the average matter distribution around galaxy pairs is a strong test for galaxy models. Utilising galaxy-galaxy-galaxy lensing as a direct probe, we map out the distribution of correlated surface mass-density around galaxy pairs in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). We have compared, for the first time, these so-called excess mass maps to predictions provided by a recent semi-analytic model, which is implanted within the dark-matter Millennium Simulation. We analysed galaxies with stellar masses between 109 − 1011 M⊙ in two photometric redshift bins, for lens redshifts z ≲ 0.6. The projected separation of the galaxy pairs ranges between 170 − 300 h−1 kpc, thereby focusing on pairs inside groups and clusters. To allow us a better interpretation of the maps, we discuss the impact of chance pairs, that is galaxy pairs that appear close to each other in projection only. We have introduced an alternative correlation map that is less affected by projection effects but has a lower signal-to-noise ratio. Our tests with synthetic data demonstrate that the patterns observed in both types of maps are essentially produced by correlated pairs which are close in redshift (Δz ≲ 5 × 10−3). We also verify the excellent accuracy of the map estimators. In an application to the galaxy samples in the CFHTLenS, we obtain a 3σ − 6σ significant detection of the excess mass and an overall good agreement with the galaxy model predictions. There are, however, a few localised spots in the maps where the observational data disagrees with the model predictions on a ≈3.5σ confidence level. Although we have no strong indications for systematic errors in the maps, this disagreement may be related to the residual B-mode pattern observed in the average of all maps. Alternatively, misaligned galaxy pairs inside dark matter halos or lensing by a misaligned distribution of the intra-cluster gas might also cause the unanticipated bulge in the distribution of the excess mass between lens pairs.\n
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".