  Current synoptic sky surveys monitor large areas of the sky to find variable
and transient astronomical sources. As the number of detections per night at a
single telescope easily exceeds several thousand, current detection pipelines
make intensive use of machine learning algorithms to classify the detected
objects and to filter out the most interesting candidates. A number of upcoming
surveys will produce up to three orders of magnitude more data, which renders
high-precision classification systems essential to reduce the manual and,
hence, expensive vetting by human experts. We present an approach based on
convolutional neural networks to discriminate between true astrophysical
sources and artefacts in reference-subtracted optical images. We show that
relatively simple networks are already competitive with state-of-the-art
systems and that their quality can further be improved via slightly deeper
networks and additional preprocessing steps -- eventually yielding models
outperforming state-of-the-art systems. In particular, our best model correctly
classifies about 97.3% of all 'real' and 99.7% of all 'bogus' instances on a
test set containing 1,942 'bogus' and 227 'real' instances in total.
Furthermore, the networks considered in this work can also successfully
classify these objects at hand without relying on difference images, which
might pave the way for future detection pipelines not containing image
subtraction steps at all.
