Related papers: Galaxy classification: a deep learning approach fo…
Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological…
A full ring is a form of galaxy morphology that is not associated with a specific stage on the Hubble sequence. Digital sky surveys can collect many millions of galaxy images, and therefore even rare forms of galaxies are expected to be…
Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies…
Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen…
Big data has become the norm in astronomy, making it an ideal domain for computer science research. Astronomers typically classify galaxies based on their morphologies, a practice that dates back to Hubble (1936). With small datasets,…
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…
We present an automated morphological classification in 4 types (E,S0,Sab,Scd) of ~700.000 galaxies from the SDSS DR7 spectroscopic sample based on support vector machines. The main new property of the classification is that we associate to…
Recent large-scale galaxy spectroscopic surveys, such as the Sloan Digital Sky Survey (SDSS), enable us to execute a systematic, relatively-unbiased search for galaxy clusters. Such surveys make it possible to measure the 3-d distribution…
The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the automation of this…
The classification of galaxy morphologies is an important step in the investigation of theories of hierarchical structure formation. While human expert visual classification remains quite effective and accurate, it cannot keep up with the…
We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artifacts. An artificial neural…
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 explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a…
The next-generation astronomy archives will cover most of the universe at fine resolution in many wavelengths. One of the first of these projects, the Sloan Digital Sky Survey (SDSS) will create a 5-wavelength catalog over 10,000 square…
The Sloan Digital Sky Survey (SDSS) is collecting photometry and intermediate resolution spectra for ~ 10**5 stars in the thick-disk and stellar halo of the Milky Way. This massive dataset can be used to infer the properties of the stars…
In many applications, Neural Nets (NNs) have classification performance on par or even exceeding human capacity. Moreover, it is likely that NNs leverage underlying features that might differ from those humans perceive to classify. Can we…
We outline here the next generation of cluster-finding algorithms. We show how advances in Computer Science and Statistics have helped develop robust, fast algorithms for finding clusters of galaxies in large multi-dimensional astronomical…
Strong Lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with Deep Learning have become a popular approach due to these astronomical…
Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful…