Related papers: Enhancing Galaxy Classification with U-Net Variati…

We propose a variant of residual networks (ResNets) for galaxy morphology classification. The variant, together with other popular convolutional neural networks (CNNs), are applied to a sample of 28790 galaxy images from Galaxy Zoo 2…

Weak gravitational lensing is a very sensitive way of measuring cosmological parameters, including dark energy, and of testing current theories of gravitation. In practice, this requires exquisite measurement of the shapes of billions of…

The high-redshift galaxy UV luminosity function (UVLF) has become essential for understanding the formation and evolution of the first galaxies. Yet, UVLFs only measure galaxy abundances, giving rise to a degeneracy between the mean galaxy…

In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into…

The increasing amount of data in astronomy provides great challenges for machine learning research. Previously, supervised learning methods achieved satisfactory recognition accuracy for the star-galaxy classification task, based on…

The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels…

We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for…

We conduct a comprehensive study on dropout galaxy candidates at $z\sim9-16$ using the first 90 arcmin$^2$ JWST/NIRCam images taken by the early release observations (ERO) and early release science (ERS) programs. With the JWST simulation…

The high-redshift progenitors of present-day galaxy clusters are believed to substantially contribute to the global star-formation rate density and drive the large-scale reionization of the Universe. Here we present a blind and unbiased…

We present the construction of an image similarity retrieval engine for the morphological classification of galaxies using the Convolutional AutoEncoder (CAE). The CAE is trained on 90,370 preprocessed Sloan Digital Sky Survey galaxy images…

We present a catalogue of photometric redshifts for galaxies from DESI Legacy Imaging Surveys, which includes $\sim0.18$ billion sources covering 14,000 ${\rm deg}^2$. The photometric redshifts, along with their uncertainties, are estimated…

This paper follows series of our works on the applicability of various machine learning methods to the morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of 315776 SDSS DR9 galaxies with absolute…

We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS…

We describe the application of the `shapelet' linear decomposition of galaxy images to multi-wavelength morphological classification using the $u,g,r,i,$ and $z$-band images of 1519 galaxies from the Sloan Digital Sky Survey. We utilize…

In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep…

Using the HST WFPC2 we perform deep I-band imaging of 9 radio-selected (limit 14 microJanskys at 8.5 GHz) faint galaxies from Roche, Lowenthal and Koo (2002). Two are also observed in V. Six of the galaxies have known redshifs of 0.4<z<1.0.…

Surveys with James Webb Space Telescope (JWST) have discovered candidate galaxies in the first 400 Myr of cosmic time. Preliminary indications have suggested these candidate galaxies may be more massive and abundant than previously thought.…

Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically…

We investigate the deconvolved color profiles of 223 disk galaxies at redshifts of $z=1$-3 observed by the James Webb Space Telescope (JWST) as part of the Cosmic Evolution Early Release Science survey (CEERS). The filters were selected to…

Edge-on galaxies have many important applications in galactic astrophysics, but they can be difficult to identify in vast amounts of astronomical data. To facilitate the search for them, we have developed a deep learning algorithm designed…