An Extended Catalogue of galaxy morphology using Deep Learning in Southern Photometric Local Universe Survey Data Release 3
C. R. Bom, A. Cortesi, U. Ribeiro, L. O. Dias, K. Kelkar, A.V. Smith, Castelli, L. Santana-Silva, V. Silva, T. S. Gon\c{c}alves, L. R. Abramo, E., V. R. Lima, F. Almeida-Fernandes, L. Espinosa, L. Li, M. L. Buzzo, C. Mendes, de Oliveira, L. Sodr\'e Jr., A. Alvarez-Candal

TL;DR
This paper presents a deep learning-based morphological catalog of over 164,000 galaxies from the Southern Photometric Local Universe Survey, achieving high accuracy in classifying galaxy types and enabling studies of galaxy properties and clustering.
Contribution
The work introduces a new deep learning method for galaxy morphology classification in S-PLUS DR3, including a quality assessment classifier, covering a large area of the southern sky.
Findings
Achieved 98.5% precision in distinguishing LT and ET galaxies.
Recovered expected color-magnitude relations for galaxy types.
Provided a comprehensive morphological catalog covering 1800 deg².
Abstract
The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work we present a Deep Learning (DL) based morphological catalog built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves an precision rate of 98.5 in accurately distinguishing between spiral, as part of the larger category of late type (LT) galaxies, and elliptical, belonging to early type (ET) galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of…
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Taxonomy
TopicsData Visualization and Analytics · Advanced Vision and Imaging · Galaxies: Formation, Evolution, Phenomena
