The miniJPAS survey: star-galaxy classification using machine learning
  Future astrophysical surveys such as J-PAS will produce very large datasets,
which will require the deployment of accurate and efficient Machine Learning
(ML) methods. In this work, we analyze the miniJPAS survey, which observed
about 1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri
broad-band filters. We discuss the classification of miniJPAS sources into
extended (galaxies) and point-like (e.g. stars) objects, a necessary step for
the subsequent scientific analyses. We aim at developing an ML classifier that
is complementary to traditional tools based on explicit modeling. In order to
train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS
and HSC-SSP data. We trained and tested 6 different ML algorithms on the two
crossmatched catalogs. As input for the ML algorithms we use the magnitudes
from the 60 filters together with their errors, with and without the
morphological parameters. We also use the mean PSF in the r detection band for
each pointing. We find that the RF and ERT algorithms perform best in all
scenarios. When analyzing the full magnitude range of 15<r<23.5 we find
AUC=0.957 with RF when using only photometric information, and AUC=0.986 with
ERT when using photometric and morphological information. Regarding feature
importance, when using morphological parameters, FWHM is the most important
feature. When using photometric information only, we observe that broad bands
are not necessarily more important than narrow bands, and errors are as
important as the measurements. ML algorithms can compete with traditional
star/galaxy classifiers, outperforming the latter at fainter magnitudes (r>21).
We use our best classifiers, with and without morphology, in order to produce a
value added catalog available at https://j-pas.org/datareleases .
