J-PLUS DR3: Galaxy-Star-Quasar classification
R. von Marttens, V. Marra, M. Quartin, L. Casarini, P.O. Baqui, A., Alvarez-Candal, F. J. Galindo-Guil, J.A. Fern\'andez-Ontiveros, Andr\'es del, Pino, L.A. D\'iaz-Garc\'ia, C. L\'opez-Sanjuan, J. Alcaniz, R. Angulo, A. J., Cenarro, D. Crist\'obal-Hornillos, R. Dupke

TL;DR
This paper develops a machine learning-based method using TPOT and XGBoost to classify sources in the J-PLUS DR3 survey into galaxies, stars, and quasars, achieving high accuracy and identifying quasars.
Contribution
It introduces an automated machine learning pipeline that outperforms existing classifiers in J-PLUS DR3, including quasar identification.
Findings
XGBoost achieves over 0.99 AUC for all classes.
The classifier surpasses previous J-PLUS DR3 methods.
Quasars are effectively identified alongside galaxies and stars.
Abstract
The Javalambre Photometric Local Universe Survey (J-PLUS) is a 12-band photometric survey using the 83-cm JAST telescope. Data Release 3 includes 47.4 million sources. J-PLUS DR3 only provides star-galaxy classification so that quasars are not identified from the other sources. Given the size of the dataset, machine learning methods could provide a valid alternative classification and a solution to the classification of quasars. Our objective is to classify J-PLUS DR3 sources into galaxies, stars and quasars, outperforming the available classifiers in each class. We use an automated machine learning tool called TPOT to find an optimized pipeline to perform the classification. The supervised machine learning algorithms are trained on the crossmatch with SDSS DR18, LAMOST DR8 and Gaia. We checked that the training set of about 660 thousand galaxies, 1.2 million stars and 270 thousand…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research
