Related papers: Merger identification through photometric bands, c…

Gravitational waves (GWs) from stellar binary black hole (sBBH) mergers can be strongly gravitational lensed by intervening galaxies/galaxy clusters. Only a few works investigated the cluster-lensed sBBH mergers by adopting oversimplified…

This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy…

Galaxy edges or truncations are low-surface-brightness (LSB) features located in the galaxy outskirts that delimit the distance up to where the gas density enables efficient star formation. As such, they could be interpreted as a…

We present the methodology and data behind the photometric redshift database of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We adopt a hybrid technique, empirically estimating the redshift via local regression on a…

Weak gravitational lensing is a powerful probe for constraining cosmological parameters, but its success relies on accurate shear measurements. In this paper, we use image simulations to investigate how a joint analysis of high-resolution…

We use a combination of deep, high angular resolution imaging data from the CDFS (HST/ACS GOODS survey) and ground based near-IR $K_s$ images to derive the evolution of the galaxy major merger rate in the redshift range $0.2 \leq z \leq…

We conduct a comprehensive study of the effects of incorporating galaxy morphology information in photometric redshift estimation. Using machine learning methods, we assess the changes in the scatter and catastrophic outlier fraction of…

The application of artificial neural networks (ANNs) for the estimation of HI gas mass fraction (\fgas) is investigated, based on a sample of 13,674 galaxies in the Sloan Digital Sky Survey (SDSS) with HI detections or upper limits from the…

We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…

Significant galaxy mergers throughout cosmic time play a fundamental role in theories of galaxy evolution. The widespread usage of human classifiers to visually assess whether galaxies are in merging systems remains a fundamental component…

Following the study of Darg et al. (2009; hereafter D09a) we explore the environments, optical colours, stellar masses, star formation and AGN activity in a sample of 3003 pairs of merging galaxies drawn from the SDSS using visual…

The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation, but the classification of galaxies in large sky surveys is becoming a significant challenge. We use data from the…

Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input…

We present single-S\'ersic two-dimensional model fits to 167,600 galaxies modelled independently in the ugrizYJHK bandpasses using reprocessed Sloan Digital Sky Survey Data Release Seven (SDSS DR7) and UKIRT Infrared Deep Sky Survey Large…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

As the third installment in a series systematically searching dual active galactic nuclei (AGN) amongst merging galaxies, we present the results of 20 dual AGNs found by using the SDSS fiber spectra. To reduce the flux contamination from…

In this paper, the fourth version the Sloan Digital Sky Survey (SDSS-4), Data Release 16 dataset was used to classify the SDSS dataset into galaxies, stars, and quasars using machine learning and deep learning architectures. We efficiently…

The estimation of the bulge and disk massses, the main baryonic components of a galaxy, can be performed using various approaches, but their implementation tend to be challenging as they often rely on strong assumptions about either the…

This study aims to improve the photometric redshifts (photo-$z$s) of galaxies by integrating two contemporary methods: template-fitting and machine learning. Finding the synergy between these two methods was not a high priority in the past,…

Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine…