  Astrophysical surveys rely heavily on the classification of sources as stars,
galaxies or quasars from multi-band photometry. Surveys in narrow-band filters
allow for greater discriminatory power, but the variety of different types and
redshifts of the objects present a challenge to standard template-based
methods. In this work, which is part of larger effort that aims at building a
catalogue of quasars from the miniJPAS survey, we present a Machine
Learning-based method that employs Convolutional Neural Networks (CNNs) to
classify point-like sources including the information in the measurement
errors. We validate our methods using data from the miniJPAS survey, a
proof-of-concept project of the J-PAS collaboration covering $\sim$ 1 deg$^2$
of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due
to the scarcity of real data, we trained our algorithms using mocks that were
purpose-built to reproduce the distributions of different types of objects that
we expect to find in the miniJPAS survey, as well as the properties of the real
observations in terms of signal and noise. We compare the performance of the
CNNs with other well-established Machine Learning classification methods based
on decision trees, finding that the CNNs improve the classification when the
measurement errors are provided as inputs. The predicted distribution of
objects in miniJPAS is consistent with the putative luminosity functions of
stars, quasars and unresolved galaxies. Our results are a proof-of-concept for
the idea that the J-PAS survey will be able to detect unprecedented numbers of
quasars with high confidence.
