  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 quasars
is both representative and contain a minimal presence of contaminants (less
than 1%). We considered 37 features: the twelve photometric bands with
respective errors, six colors, four morphological parameters, galactic
extinction with its error and the PSF relative to the corresponding pointing.
With TPOT genetic algorithm, we found that XGBoost provides the best
performance: the AUC for galaxies, stars and quasars is above 0.99 and the
average precision is above 0.99 for galaxies and stars and 0.96 for quasars.
XGBoost outperforms the classifiers already provided in J-PLUS DR3 and also
classifies quasars.
