Bibliographic record
Abstract
Weak lensing convergence peaks are a promising tool to probe nonlinear structure evolution at late times, providing additional cosmological information beyond second-order statistics. Previous theoretical and observational studies have shown that the cosmological constraints on ${\mathrm{\ensuremath{\Omega}}}_{m}$ and ${\ensuremath{\sigma}}_{8}$ are improved by a factor of up to $\ensuremath{\approx}2$ when peak counts and second-order statistics are combined, compared to using the latter alone. We study the origin of lensing peaks using observational data from the $154\text{ }\text{ }{\mathrm{deg}}^{2}$ Canada-France-Hawaii Telescope Lensing Survey. We found that while high peaks (with height $\ensuremath{\kappa}>3.5{\ensuremath{\sigma}}_{\ensuremath{\kappa}}$, where ${\ensuremath{\sigma}}_{\ensuremath{\kappa}}$ is the rms of the convergence $\ensuremath{\kappa}$) are typically due to one single massive halo of $\ensuremath{\approx}1{0}^{15}{M}_{\ensuremath{\bigodot}}$, low peaks ($\ensuremath{\kappa}\ensuremath{\lesssim}{\ensuremath{\sigma}}_{\ensuremath{\kappa}}$) are associated with constellations of 2--8 smaller halos ($\ensuremath{\lesssim}1{0}^{13}{M}_{\ensuremath{\bigodot}}$). In addition, halos responsible for forming low peaks are found to be significantly offset from the line of sight towards the peak center (impact parameter $\ensuremath{\gtrsim}$ their virial radii), compared with $\ensuremath{\approx}0.25$ virial radii for halos linked with high peaks, hinting that low peaks are more immune to baryonic processes whose impact is confined to the inner regions of the dark matter halos. Our findings are in good agreement with results from the simulation work by Yang et al. [Phys. Rev. D 84, 043529 (2011)].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".