6 papers
Cosmological Constraints from Bias-Robust Wavelet Scattering Statistics for Stage-IV Galaxy Surveys
Zhujun Jiang, Xu Xiao, Zhiwei Min +5
A central challenge in precision cosmology with galaxy surveys is to extract non-Gaussian information from large-scale structure while controlling systematic uncertainties such as…
Extracting Cosmological Information from Lightcone Data: A Comparison of CNNs and Summary-Statistic-Based Approaches
Min Zhiwei, Xiao Xu, Jiang Zhujun +9
Lightcone observations are the natural data format of galaxy surveys, but their evolving geometry breaks the translational symmetry assumed by standard convolutional neural network…
Enhancing cosmological constraints with nonlinear tanh transformations of Hermite-Gaussian Derivative fields
Zhiwei Min, Ye Ma, Zhujun Jiang +4
A key goal in large-scale structure analysis is to extract multi-scale information to improve cosmological parameter constraints. In particular, higher-order derivative fields are…
A New Wavelet Scattering Transform-Based Statistic for Cosmological Analysis of Large-Scale Structure
Zhujun Jiang, Xiaolin Luo, Wenying Du +6
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…
Enhancing Cosmological Constraints by Two-dimensional -cosmic-web Weighted Angular Correlation Functions
Fenfen Yin, Liang Xiao, Wenying Du +6
In this study, we investigate the potential of mark-weighted angular correlation functions (MACFs), which integrate -cosmic-web classification with angular correlation function…
Deep learning for cosmological parameter inference from a dark matter halo density field
Zhiwei Min, Xu Xiao, Jiacheng Ding +11
We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and assoc…