How to Identify and Separate Bright Galaxy Clusters from the Low-frequency Radio Sky?
Jingying Wang, Haiguang Xu, Junhua Gu, Tao An, Haijuan Cui, Jianxun, Li, Zhongli Zhang, Qian Zheng, and Xiang-Ping Wu

TL;DR
This study simulates the low-frequency radio sky to develop a method for identifying and separating bright galaxy clusters from foreground emissions, enhancing the potential for cosmological and astrophysical insights.
Contribution
The paper introduces a novel approach combining ICA and wavelet detection to effectively identify bright galaxy clusters in low-frequency radio observations.
Findings
Approximately 80% of bright galaxy clusters can be identified with one month of 21CMA data.
Morphological and spectral distortions in detected clusters are minimal, ensuring reliable separation.
The method enables future studies of cluster properties and cosmic reionization with improved foreground removal.
Abstract
In this work we simulate the MHz radio sky that is constrained in the field of view ( radius) of the 21 Centimeter Array (21CMA), by carrying out Monte-Carlo simulations to model redshifted cosmological reionization signals and strong contaminating foregrounds, including emissions from our Galaxy, galaxy clusters, and extragalactic point sources. As an improvement of previous works, we consider in detail not only random variations of morphological and spectroscopic parameters within the ranges allowed by multi-band observations, but also evolution of radio halos in galaxy clusters, assuming that relativistic electrons are re-accelerated in the ICM in merger events and lose energy via both synchrotron emission and inverse Compton scattering with CMB photons. By introducing a new approach designed on the basis of independent component analysis (ICA) and wavelet…
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