Weak lensing scattering transform: dark energy and neutrino mass sensitivity

TL;DR

This paper demonstrates that the scattering transform, a neural network-inspired statistical method, enhances the extraction of non-Gaussian information from weak lensing maps, improving constraints on dark energy and neutrino mass.

Contribution

It extends previous work by applying the scattering transform to weak lensing data, showing its superior sensitivity and better likelihood properties compared to traditional statistics.

Findings

Scattering transform outperforms power spectrum and bispectrum in characterizing lensing maps.

It provides higher sensitivity to dark energy and neutrino mass parameters in noiseless conditions.

Scattering coefficients have more Gaussian sampling distributions, aiding cosmological inference.

Abstract

As weak lensing surveys become deeper, they reveal more non-Gaussian aspects of the convergence field which can only be extracted using statistics beyond the power spectrum. In Cheng et al. (2020) we showed that the scattering transform, a novel statistic borrowing mathematical concepts from convolutional neural networks, is a powerful tool for cosmological parameter estimation in the non-Gaussian regime. Here, we extend that analysis to explore its sensitivity to dark energy and neutrino mass parameters with weak lensing surveys. We first use image synthesis to show visually that, compared to the power spectrum and bispectrum, the scattering transform provides a better statistical vocabulary to characterize the perceptual properties of lensing mass maps. We then show that it is also better suited for parameter inference: (i) it provides higher sensitivity in the noiseless regime, and (ii) at the noise level of Rubin-like surveys, though the constraints are not significantly tighter than those of the bispectrum, the scattering coefficients have a more Gaussian sampling distribution, which is an important property for likelihood parametrization and accurate cosmological inference. We argue that the scattering coefficients are preferred statistics considering both constraining power and likelihood properties.