Conventional galaxy mass estimation methods suffer from model assumptions and
degeneracies. Machine learning (ML), which reduces the reliance on such assumptions, can …

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

Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of
reasonably realistic synthetic galaxies across cosmic time. However, quantitatively …

The field of artificial intelligence based image enhancement has been rapidly evolving over
the last few years and is able to produce impressive results on non-astronomical images. In …

Traffic accidents threaten human lives and impose substantial financial burdens annually.
Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and …

Context. Galaxy clusters grow through mergers and the accretion of substructures along
large-scale filaments. Many of the missing baryons in the local Universe may reside in such …

We investigate the importance of various dynamical features in predicting the dynamical
state (ds) of galaxy clusters, based on the Random Forest (RF) machine-learning approach …

The total masses of galaxy clusters characterize many aspects of astrophysics and the
underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for …

We present a systematic study of X-ray cavities using archival Chandra observations of
nearby galaxy clusters selected by their Sunyaev–Zel'dovich (SZ) signature in the Planck …

Artificial neural networks (ANNs) have been successfully used in the last years to identify
patterns in astronomical images. The use of ANN in the field of asteroid dynamics has been …