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Probing Cosmology with Weak Lensing Minkowski Functionals

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
abstract

In this paper, we show that Minkowski Functionals (MFs) of weak gravitational lensing (WL) convergence maps contain significant non-Gaussian, cosmology-dependent information. To do this, we use a large suite of cosmological ray-tracing N-body simulations to create mock WL convergence maps, and study the cosmological information content of MFs derived from these maps. Our suite consists of 80 independent 512^3 N-body runs, covering seven different cosmologies, varying three cosmological parameters Omega_m, w, and sigma_8 one at a time, around a fiducial LambdaCDM model. In each cosmology, we use ray-tracing to create a thousand pseudo-independent 12 deg^2 convergence maps, and use these in a Monte Carlo procedure to estimate the joint confidence contours on the above three parameters. We include redshift tomography at three different source redshifts z_s=1, 1.5, 2, explore five different smoothing scales theta_G=1, 2, 3, 5, 10 arcmin, and explicitly compare and combine the MFs with the WL power spectrum. We find that the MFs capture a substantial amount of information from non-Gaussian features of convergence maps, i.e. beyond the power spectrum. The MFs are particularly well suited to break degeneracies and to constrain the dark energy equation of state parameter w (by a factor of ~ three better than from the power spectrum alone). The non-Gaussian information derives partly from the one-point function of the convergence (through V_0, the "area" MF), and partly through non-linear spatial information (through combining different smoothing scales for V_0, and through V_1 and V_2, the boundary length and genus MFs, respectively). In contrast to the power spectrum, the best constraints from the MFs are obtained only when multiple smoothing scales are combined.

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fields

astro-ph.CO 2

years

2026 1 2025 1

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UNVERDICTED 2

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representative citing papers

The first AKRA mass map reconstruction from HSC Y1 data

astro-ph.CO · 2025-11-16 · unverdicted · novelty 6.0

AKRA produces the first unbiased kappa maps from HSC Y1 shear catalogs, with simulation tests confirming no bias in power spectrum, variance, skewness, and PDF statistics.

Machine-learning applications for weak-lensing cosmology

astro-ph.CO · 2026-05-13 · 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.

citing papers explorer

Showing 2 of 2 citing papers.

  • The first AKRA mass map reconstruction from HSC Y1 data astro-ph.CO · 2025-11-16 · unverdicted · none · ref 29 · internal anchor

    AKRA produces the first unbiased kappa maps from HSC Y1 shear catalogs, with simulation tests confirming no bias in power spectrum, variance, skewness, and PDF statistics.

  • Machine-learning applications for weak-lensing cosmology astro-ph.CO · 2026-05-13 · unverdicted · none · ref 43 · internal anchor

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