Probing Cosmology through Higher-Order CMB Lensing Statistics
Abstract
We investigate the cosmological information in higher-order statistics of the cosmic microwave background (CMB) lensing convergence field for a near-term experiment with noise properties similar to the Simons Observatory (SO). Using a fully field-level forward-modeling pipeline based on ray-traced simulations from the MassiveNuS suite and realistic SO-like CMB lensing reconstruction, we naturally include nonlinear structure formation, post-Born effects, and higher-order reconstruction noise. We measure several non-Gaussian statistics, including Minkowski functionals, peak and minima counts, moments, and wavelet-scattering coefficients. We train Gaussian-process emulators to model each statistic's dependence on the matter density fraction , the scalar power spectrum amplitude , and the neutrino mass sum . We quantify the relative information gain these statistics provide beyond the lensing power spectrum and identify which are most robust to reconstruction noise. We find that morphology-based statistics, particularly Minkowski functionals and peak/minima counts, offer significant complementary constraining power: combining all non-Gaussian statistics with the power spectrum yields reductions of 40% and 38% in the marginalized uncertainties on and , respectively, and a 70% reduction in the one-sided uncertainty on . These gains remain non-negligible even when the power spectrum is extended to larger scales and combined with primary CMB and BAO data, with Minkowski functionals providing an additional 11% improvement in and 35% in beyond the extended power spectrum. By contrast, moments and wavelet-scattering coefficients provide more limited gains at SO noise levels. Our results highlight the potential of non-Gaussian statistics to enhance cosmological constraints from SO and future CMB surveys.
Cite
@article{arxiv.2603.12335,
title = {Probing Cosmology through Higher-Order CMB Lensing Statistics},
author = {Shu-Fan Chen and J. Colin Hill and Zoltán Haiman},
journal= {arXiv preprint arXiv:2603.12335},
year = {2026}
}
Comments
27 pages, 21 figures. Comments welcome!