A New Wavelet Scattering Transform-Based Statistic for Cosmological Analysis of Large-Scale Structure

Zhujun Jiang; Xiaolin Luo; Wenying Du; Zhiwei Min; Fenfen Yin; Longlong Feng; Jiacheng Ding; Le Zhang; Xiao-Dong Li

doi:10.3847/1538-4357/ae04e9

paper

A New Wavelet Scattering Transform-Based Statistic for Cosmological Analysis of Large-Scale Structure

arXiv:2505.14400 · doi:10.3847/1538-4357/ae04e9

Abstract

Large-scale structure (LSS) analysis in galaxy surveys is a powerful cosmological probe but is limited by tracer bias, which can obscure underlying information and weaken parameter constraints. Existing methods either model bias or restrict analyses to low-density regions, yet their sensitivity to bias remains poorly understood. We propose a novel method based on the wavelet scattering transform (WST) to distinguish LSS across cosmological models while mitigating tracer bias. Central to our approach are the WST $m$ -mode ratios, $R^{wst}$ , a new statistical measure, and a high-density apodization preprocessing that smoothly rescales extreme values. We use a reduced chi-square to assess the cosmological parameter constraints and find that $R^{wst}$ , in the scale range $j \in [3, 7]$ , achieves $χ_{ν, cos}^{2} \approx 6$ for cosmology while maintaining $χ_{ν, bias}^{2} \sim 1$ --a regime unattained by other statistics. $R^{wst}$ thus provides robust cosmological sensitivity with effective bias mitigation for future surveys.

16 pages, 5 figures

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