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20172022
most citedMachine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study

93 citations · 215 across the 10 of their papers we have counts for

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Showing astro-ph.GAShow all

6 papers · 1 filter

astro-ph.GA2020★ 5 cited

Stellar Population Properties of ETGs in Compact Groups of Galaxies

Tatiana C. Moura, Reinaldo R. de Carvalho, Sandro B. Rembold +6

We present results on the study of the stellar population in Early-Type galaxies (ETGs) belonging to 151 Compact Groups (CGs). We also selected a field sample composed of 846 ETGs…

astro-ph.GA2019★ 7 cited

The Mass Density Profile and Star Formation History of Gaussian and Non-Gaussian Clusters

R. R. de Carvalho, A. P. Costa, T. C. Moura +1

This paper is the third of a series in which we investigate the discrimination between Gaussian (G) and Non-Gaussian (NG) clusters, based on the velocity distribution of the member…

astro-ph.GA2018★ 25 cited

Gradient Pattern Analysis Applied to Galaxy Morphology

Reinaldo. R. Rosa, Reinaldo. R. de Carvalho, Rubens. A. Sautter +6

Gradient pattern analysis (GPA) is a well-established technique for measuring gradient bilateral asymmetries of a square numerical lattice. This paper introduces an improved versio…

astro-ph.GA2017

Bayesian surface photometry analysis for early-type galaxies

D. H. Stalder, Reinaldo R. de Carvalho, Martin D. Weinberg +4

We explore the application of Bayesian image analysis to infer the properties of an SDSS early-type galaxy sample including AGN. We use GALPHAT (Yoon et al. 2010) with a Bayes-fact…

astro-ph.GA2017★ 35 cited

Investigating the Relation between Galaxy Properties and the Gaussianity of the Velocity Distribution of Groups and Clusters

R. R. de Carvalho, A. L. B. Ribeiro, D. Stalder +3

We investigate the dependence of stellar population properties of galaxies on group dynamical stage for a subsample of Yang catalog. We classify groups according to their galaxy ve…

astro-ph.GA2017★ 11 cited

Improving galaxy morphology with machine learning

P. H. Barchi, F. G. da Costa, R. Sautter +4

This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN)…