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The X-ray regime, where the most massive visible component of galaxy clusters, the intra cluster medium (ICM), is visible, offers directly measured quantities, like the luminosity, and derived quantities, like the total mass, to…
In our previous works, we proposed a machine learning framework named \texttt{USmorph} for efficiently classifying galaxy morphology. In this study, we propose a self-supervised method called contrastive learning to upgrade the unsupervised…
The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
We use high-precision photometry of red-giant-branch (RGB) stars in 57 Galactic globular clusters (GCs), mostly from the `Hubble Space Telescope (HST) UV Legacy Survey of Galactic globular clusters', to identify and characterize their…
We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based on various dark matter halo and galaxy features. The RF is trained and tested using…
We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study…
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for…
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only…
We present a two-component Machine Learning (ML) based approach for classifying astronomical images by data-quality via an examination of sources detected in the images and image pixel values from representative sources within those images.…
The Small Magellanic Cloud (SMC), a satellite galaxy of the Milky Way, is an irregular dwarf galaxy exhibiting evidence of recent and ongoing star formation. We performed a spatial clustering analysis of far-ultraviolet stars in the SMC…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…
Components of large-scale structure (LSS) of Universe includes galaxy clusters, walls, filaments, groups and field galaxies. The question of spatial organization of all these components remains open despite of a number of recently developed…
The future Rubin Legacy Survey of Space and Time (LSST) is expected to deliver its first data release in the current of 2025. The upcoming survey will provide us with images of galaxy clusters in the optical to the near-infrared, with…
With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic. Available automatic galaxy image…
To ascertain whether photometric decompositions of galaxies into bulges and disks are astrophysically meaningful, we have developed a new technique to decompose spectral data cubes into separate bulge and disk components, subject only to…
Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to…
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce unprecedentedly deep and wide photometric catalogs, enabling transformative studies of faint stellar systems such as the research of ultra-faint dwarf…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
We use automated surface photometry and pattern classification techniques to morphologically classify galaxies. The two-dimensional light distribution of a galaxy is reconstructed using Fourier series fits to azimuthal profiles computed in…