  Point source detection at low signal-to-noise is challenging for astronomical
surveys, particularly in radio interferometry images where the noise is
correlated. Machine learning is a promising solution, allowing the development
of algorithms tailored to specific telescope arrays and science cases. We
present DeepSource - a deep learning solution - that uses convolutional neural
networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio
(SNR) of the original map and then uses dynamic blob detection to detect
sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT
images with a total of 300,000 sources, DeepSource is essentially perfect in
both purity and completeness down to SNR = 4 and outperforms PyBDSF in all
metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC)
score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For
natural-weighting we find a smaller improvement of ~40% in the PC score at SNR
= 3. If instead we ask where either of the purity or completeness first drop to
90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the
4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can
learn to optimally trade off purity and completeness for any science case under
consideration. Our results show that deep learning is a promising approach to
point source detection in astronomical images.
