KiDS-450: cosmological constraints from weak-lensing peak statistics – II: Inference from shear peaks using N-body simulations
Bibliographic record
Abstract
We study the statistics of peaks in a weak-lensing reconstructed mass map of the first 450 deg2 of the Kilo Degree Survey (KiDS-450). The map is computed with aperture masses directly applied to the shear field with an NFW-like compensated filter. We compare the peak statistics in the observations with that of simulations for various cosmologies to constrain the cosmological parameter |$S_8 = \sigma _8 \sqrt{\Omega _{\rm m}/0.3}$|, which probes the (Ωm, σ8) plane perpendicularly to its main degeneracy. We estimate S8 = 0.750 ± 0.059, using peaks in the signal-to-noise range 0 ≤ S/N ≤ 4, and accounting for various systematics, such as multiplicative shear bias, mean redshift bias, baryon feedback, intrinsic alignment, and shear–position coupling. These constraints are ∼ 25 per cent tighter than the constraints from the high significance peaks alone (3 ≤ S/N ≤ 4) which typically trace single-massive haloes. This demonstrates the gain of information from low-S/N peaks. However, we find that including S/N < 0 peaks does not add further information. Our results are in good agreement with the tomographic shear two-point correlation function measurement in KiDS-450. Combining shear peaks with non-tomographic measurements of the shear two-point correlation functions yields a ∼20 per cent improvement in the uncertainty on S8 compared to the shear two-point correlation functions alone, highlighting the great potential of peaks as a cosmological probe.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".