STELLAR POPULATION PHOTOMETRIC SYNTHESIS WITH ARTIFICIAL INTELLIGENCE OF S-PLUS GALAXIES

Authors

  • V. Cernic 1Universidade de São Paulo, Instituto de Astronomia, Geofísica e Ciências Atmosférica

Keywords:

galaxies, stellar populations, machine learning

Abstract

We trained a Neural Network that can obtain select STARLIGHT parameters directly from S-PLUS photometric data. The training set consisted of over 55 thousand galaxies with their stellar population parameters obtained from an application of STARLIGHT by Cid Fernandes et al. [1]. These galaxies were crossmatched with the S-PLUS iDR 3 database, thus, recovering the photometry for the 12 band filters for 55803 objects. We also considered the spectroscopic redshift for each object which was obtained from the SDSS. Finally, we trained a fully connected Neural Network with the 12 photometries plus the redshift as features and targeted some of the STARLIGHT parameters, such as stellar mass and mean stellar age. The model performed very well for some parameters, for example, the stellar mass, with an error of 0.23 dex. In the following months, we aim to apply the whole S-PLUS database to this trained model, obtaining never-before-seen photometric synthesis for most objects in the catalogue.

Author Biography

V. Cernic, 1Universidade de São Paulo, Instituto de Astronomia, Geofísica e Ciências Atmosférica

for the S-PLUS collaboration

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Published

2021-12-10

How to Cite

Cernic, V. (2021). STELLAR POPULATION PHOTOMETRIC SYNTHESIS WITH ARTIFICIAL INTELLIGENCE OF S-PLUS GALAXIES. Journal of Production and Automation (JPAUT) ISSN 2595-9573, 4(2), 8–11. Retrieved from https://jpaut.com.br/index.php/jpaut/article/view/50