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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Machine and Deep Learning applied to galaxy morphology - A comparative study

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Barchi, P. H. [1, 2] ; de Carvalho, R. R. [3, 4] ; Rosa, R. R. [1] ; Sautter, R. A. [1] ; Soares-Santos, M. [2] ; Marques, B. A. D. [5] ; Clua, E. [5] ; Goncalves, T. S. [6] ; de Sa-Freitas, C. [6] ; Moura, T. C. [7]
Total Authors: 10
[1] Natl Inst Space Res INPE, Lab Comp & Appl Math, Av Astronautas 1-758, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[2] Brandeis Univ, Phys Dept, Waltham, MA 02254 - USA
[3] Natl Inst Space Res INPE, Astrophys Div, Sao Jose Dos Campos - Brazil
[4] Univ Cidade Sao Paulo, NAT Univ Cruzeiro Sul, Sao Paulo - Brazil
[5] Fed Fluminense Univ UFF, Inst Comp, Niteroi, RJ - Brazil
[6] Fed Univ Rio De Janeiro UFRJ, Valongo Observ, Rio De Janeiro - Brazil
[7] Sao Paulo Univ USP, Inst Astron Geofis & Ciencias Atmosfer IAG, Sao Paulo - Brazil
Total Affiliations: 7
Document type: Journal article
Web of Science Citations: 0

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification. We combine accurate visual classifications from the Galaxy Zoo project with machine and deep learning methodologies. We propose two distinct approaches for galaxy morphology: one based on non-parametric morphology and traditional machine learning algorithms; and another based on Deep Learning. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to study galaxy morphology. The main datasets employed comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also discuss the class imbalance problem considering three classes. Performance of each model is mainly measured by Overall Accuracy (OA). A spectroscopic validation with astrophysical parameters is also provided for Decision Tree models to assess the quality of our morphological classification. In all of our samples, both Deep and Traditional Machine Learning approaches have over 94.5% OA to classify galaxies in two classes (elliptical and spiral). We compare our classification with state-of-the-art morphological classification from literature. Considering only two classes separation, we achieve 99% of overall accuracy in average when using our deep learning models, and 82% when using three classes. We provide a catalog with 670,560 galaxies containing our best results, including morphological metrics and classification. (C) 2019 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 14/11156-4 - What drives the stellar mass growth of Early-Type galaxies? Born or made: the saga continues
Grantee:Reinaldo Ramos de Carvalho
Support type: Research Projects - Thematic Grants