Constraining neutrino mass with tomographic weak lensing peak counts

TL;DR

This paper demonstrates that using weak lensing peak counts, which capture non-Gaussian features, improves constraints on neutrino mass from galaxy survey data beyond traditional methods.

Contribution

It introduces a novel approach combining peak counts with power spectrum analysis to better constrain neutrino mass in cosmological data.

Findings

Peak counts improve neutrino mass constraints by up to 39%.

Combining peak counts with power spectrum enhances parameter estimation accuracy.

Non-Gaussian statistics provide additional information beyond second order statistics.

Abstract

Massive cosmic neutrinos change the structure formation history by suppressing perturbations on small scales. Weak lensing data from galaxy surveys probe the structure evolution and thereby can be used to constrain the total mass of the three active neutrinos. However, much of the information is at small scales where the dynamics are nonlinear. Traditional approaches with second order statistics thus fail to fully extract the information in the lensing field. In this paper, we study constraints on the neutrino mass sum using lensing peak counts, a statistic which captures non-Gaussian information beyond the second order. We use the ray-traced weak lensing mocks from the Cosmological Massive Neutrino Simulations (MassiveNuS), and apply LSST-like noise. We discuss the effects of redshift tomography, multipole cutoff $\ell_{\rm max}$ for the power spectrum, smoothing scale for the peak counts, and constraints from peaks of different heights. We find that combining peak counts with the traditional lensing power spectrum can improve the constraint on neutrino mass sum, $\Omega_m$, and $A_s$ by 39%, 32%, and 60%, respectively, over that from the power spectrum alone.