2025/05/20 by Jiang, Zhujun, Xiaolin Luo, Luo, Xiaolin +14
Economics, Econometrics and Finance · Physics and Astronomy · #Advanced Mathematical Theories and Applications #Complex Systems and Time Series Analysis #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences
paper · pdf · doi:10.48550/arxiv.2505.14400
openalex publication_date 2025/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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\rm 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\rm wst, in the scale range j ∈ [3,7], achieves χ2ν, \rm cos ≈ 6 for cosmology while maintaining χ2ν, \rm bias ∼ 1--a regime unattained by other statistics. R\rm wst thus provides robust cosmological sensitivity with effective bias mitigation for future surveys.