The S-PLUS Fornax Project (S+FP): An extragalactic catalog covering $\sim$ 5 virial radii around NGC 1399 with galaxy properties

TL;DR

This paper presents a comprehensive galaxy catalog around NGC 1399 using S-PLUS data, employing deep learning for classification and estimating galaxy properties for large-scale structure studies.

Contribution

It introduces a new galaxy catalog covering the Fornax cluster region with advanced classification and property estimation methods using deep learning and multi-wavelength data.

Findings

Catalog contains 119,580 galaxies with multi-band photometry.

Photometric redshifts estimated with σ_NMAD ~ 0.0219.

Stellar masses, SFRs, and D4000_N indices derived via machine learning.

Abstract

Observational extragalactic catalogs over wide sky areas are essential for uncovering the large-scale structure of the Universe. They allow, among others, cosmological studies and density analyses that impose strong constraints on models of galaxy formation and evolution. By taking advantage of the wide field images and the 12 optical bands of the Southern Photometric Local Universe Survey (S-PLUS), we aim at providing a catalog of galaxies located, in projection, towards the Fornax galaxy cluster, within $\sim$ 5 virial radii in right ascension (R.A.) and $\sim$ 3 virial radius in declination (Dec) around NGC,1399, the dominant galaxy of the cluster. Such a catalog will allow unprecedented large-scale structure studies in that sky region. Supervised deep learning algorithms have been developed, utilizing neural networks complemented with dimensionality reduction techniques, to classify and separate spurious objects, stars and galaxies in a photometric catalog previously built for the S-PLUS Fornax Project (S+FP). That catalog was built using a combination of SExtractor configurations optimized for galaxy detection and characterization. A catalog of 119,580 galaxies was obtained in the direction of the Fornax cluster containing photometric information in the 12 optical bands of S-PLUS complemented with GALEX (UV), VHS-VISTA (NIR) and AllWISE (MIR) data. We estimate photometric redshifts ({\sigma}_ NMAD $\sim$ 0.0219) with a lower limit of z_ lim $\sim$ 0.03. Stellar masses, star formation rates (SFRs) and D4000_N index estimates were obtained through a machine learning approach, by matching S-PLUS photometric data to SDSS spectroscopic data. The completeness of the catalog (72%) was calculated by comparing with mock catalogs ...