Simultaneous Dependence of Matter Clustering on Scale and Environment
Yun Wang, Ping He

TL;DR
This paper introduces new statistical tools to analyze how matter clustering depends simultaneously on scale and environment, revealing environment-specific behaviors in dark matter and gas distributions across cosmic epochs.
Contribution
The work develops environment-dependent wavelet-based statistics to characterize matter clustering, providing novel insights into the environmental dependence of dark matter and gas distributions.
Findings
Clustering strength increases with density for dark matter and gas.
Gas is less biased in dense environments at low redshift due to reaccretion.
Dark matter and gas are more tightly correlated in extreme environments.
Abstract
In this work, we propose new statistical tools that are capable of characterizing the simultaneous dependence of dark matter and gas clustering on the scale and the density environment, and these are the environment-dependent wavelet power spectrum (env-WPS), the environment-dependent bias function (env-bias), and the environment-dependent wavelet cross-correlation function (env-WCC). These statistics are applied to the dark matter and baryonic gas density fields of the \texttt{TNG100-1} simulation at redshifts of -, and to \texttt{Illustris-1} and \texttt{SIMBA} at . The measurements of the env-WPSs suggest that the clustering strengths of both the dark matter and the gas increase with increasing density, while that of a Gaussian field shows no density dependence. By measuring the env-bias and env-WCC, we find that they vary significantly with the environment, scale,…
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Taxonomy
TopicsAdvanced Clustering Algorithms Research
