The distribution of baryons provides a significant way to understand the formation of galaxy
clusters by revealing the details of its internal structure and changes over time. In this paper …

We evaluate the effectiveness of deep learning (DL) models for reconstructing the masses of
galaxy clusters using X-ray photometry data from next-generation surveys. We establish …

Complex astrophysical systems often exhibit low-scatter relations between observable
properties (eg, luminosity, velocity dispersion, oscillation period). These scaling relations …

We develop a machine learning algorithm to infer the three-dimensional cumulative radial
profiles of total and gas masses in galaxy clusters from thermal Sunyaev–Zel'dovich effect …

ABSTRACT A galaxy cluster as the most massive gravitationally bound object in the
Universe, is dominated by dark matter, which unfortunately can only be investigated through …

Temperature profiles of the hot galaxy cluster intracluster medium (ICM) have a complex non-
linear structure that traditional parametric modelling may fail to fully approximate. For this …

Context. Galaxy clusters are the largest gravitating structures in the universe, and their mass
assembly is sensitive to the underlying cosmology. Their mass function, baryon fraction, and …

Machine learning has rapidly become a tool of choice for the astronomical community. It is
being applied across a wide range of wavelengths and problems, from the classification of …

We demonstrate that pairwise peculiar velocity correlations for galaxy clusters can be
directly reconstructed from the kinematic Sunyaev-Zel'dovich (kSZ) signature imprinted in …

We present a novel approach to identify galaxy clusters that are undergoing a merger using
a deep learning approach. This paper uses massive galaxy clusters spanning 0≤ z≤ 2 from …