pith. machine review for the scientific record. sign in

arxiv: 1606.01318 · v2 · submitted 2016-06-04 · 🌌 astro-ph.CO

Recognition: unknown

The Origin of Weak Lensing Convergence Peaks

Authors on Pith no claims yet
classification 🌌 astro-ph.CO
keywords peakskappahaloslensingsigmaconvergencecomparedcosmological
0
0 comments X
read the original 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 $\Omega_m$ and $\sigma_8$ are improved by a factor of up to ~ 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 deg$^2$ Canada-France-Hawaii Telescope Lensing Survey. We found that while high peaks (with height $\kappa$ >3.5 $\sigma_\kappa$, where $\sigma_\kappa$ is the r.m.s. of the convergence $\kappa$) are typically due to one single massive halo of ~$10^{15}M_\odot$, low peaks ($\kappa$ <~ $\sigma_\kappa$) are associated with constellations of 2-8 smaller halos (<~$10^{13}M_\odot$). 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 >~ their virial radii), compared with ~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 el al. (2011).

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Machine-learning applications for weak-lensing cosmology

    astro-ph.CO 2026-05 unverdicted novelty 2.0

    Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.