Dark Energy Survey Year 3 results: simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps II. Cosmological results
M. Gatti, G. Campailla, N. Jeffrey, L. Whiteway, A. Porredon, J. Prat,, J. Williamson, M. Raveri, B. Jain, V. Ajani, G. Giannini, M. Yamamoto, C., Zhou, J. Blazek, D. Anbajagane, S. Samuroff, T. Kacprzak, A. Alarcon, A., Amon, K. Bechtol, M. Becker, G. Bernstein, A. Campos

TL;DR
This paper uses advanced statistical methods and simulations to analyze weak lensing data from DES Y3, significantly improving constraints on cosmological parameters like S_8 by combining Gaussian and non-Gaussian statistics.
Contribution
It introduces a simulation-based framework combining multiple non-Gaussian statistics and neural network compression to enhance cosmological parameter estimation from weak lensing maps.
Findings
70-90% improvement in FoM with non-Gaussian statistics
Achieved 2% precision on S_8 parameter
Results are consistent with previous DES and Planck data
Abstract
We present a simulation-based cosmological analysis using a combination of Gaussian and non-Gaussian statistics of the weak lensing mass (convergence) maps from the first three years (Y3) of the Dark Energy Survey (DES). We implement: 1) second and third moments; 2) wavelet phase harmonics; 3) the scattering transform. Our analysis is fully based on simulations, spans a space of seven CDM cosmological parameters, and forward models the most relevant sources of systematics inherent in the data: masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration. We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a simulation-based inference approach. Including and combining different non-Gaussian statistics is a powerful tool that strongly improves constraints over Gaussian…
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