Cluster cosmology with anisotropic boosts: validation of a novel forward modelling analysis and application on <i>SDSS</i> redMaPPer clusters
Bibliographic record
Abstract
ABSTRACT We present a novel analysis for cluster cosmology that fully forward models the abundances, weak lensing, and the clustering of galaxy clusters. Our analysis notably includes an empirical model for the anisotropic boosts impacting the lensing and clustering signals of optical clusters. These boosts arise from a preferential selection of clusters surrounded by anisotropic large-scale structure, a consequence of the limited discrimination between line-of-sight interlopers and true cluster members offered by photometric surveys. We validate our analysis via a blind cosmology challenge on mocks, and find that we can obtain tight and unbiased cosmological constraints without informative priors or external calibrations on any of our model parameters. We then apply our analysis on the SDSS redMaPPer clusters, and find results favoring low Ωm and high σ8, combining to yield the lensing strength constraint $S_8 = 0.715_{-0.021}^{+0.024}$. We investigate potential drivers behind these results through a series of post-unblinding tests, noting that our results are consistent with existing cluster cosmology constraints but clearly inconsistent with other CMB/LSS based cosmology results. From these tests, we find hints that a suppression in the cluster lensing signal may be driving our results.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".