Continuous wavelet analysis of matter clustering using the Gaussian-derived wavelet
Yun Wang, Hua-Yu Yang, Ping He

TL;DR
This paper introduces wavelet-based statistical methods to analyze matter clustering in large-scale structures, applying them to simulations and revealing environmental influences on clustering strength and matter distribution.
Contribution
It develops wavelet power spectrum and bicoherence techniques for matter clustering analysis and applies them to cosmological simulations, highlighting environmental effects.
Findings
Clustering strength increases with local density on most scales.
Gas traces dark matter better than stars on large scales.
AGN feedback impacts matter distribution differently across environments.
Abstract
Continuous wavelet analysis has been increasingly employed in various fields of science and engineering due to its remarkable ability to maintain optimal resolution in both space and scale. Here, we introduce wavelet-based statistics, including the wavelet power spectrum, wavelet cross-correlation and wavelet bicoherence, to analyze the large-scale clustering of matter. For this purpose, we perform wavelet transforms on the density distribution obtained from the one-dimensional Zel'dovich approximation and then measure the wavelet power spectra and wavelet bicoherences of this density distribution. Our results suggest that the wavelet power spectrum and wavelet bicoherence can identify the effects of local environments on the clustering at different scales. Moreover, we apply the statistics based on the three-dimensional isotropic wavelet to the IllustrisTNG simulation at z = 0, and…
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