  We demonstrate two new approaches that have been developed to aid the
production of future hard X-ray catalogs, and specifically to reduce the
reliance on human intervention during the detection of faint excesses in maps
that also contain systematic noise. A convolutional neural network has been
trained on data from the INTEGRAL/ISGRI telescope to create a source detection
tool that is more sensitive than previous methods, whilst taking less time to
apply to the data and reducing the human subjectivity involved in the process.
This new tool also enables searches on smaller observation timescales than was
previously possible. We show that a method based on Bayesian reasoning is
better able to combine the detections from multiple observations than previous
methods. When applied to data from the first 1000 INTEGRAL revolutions these
improved techniques detect 25 sources (about 5% of the total sources) which
were previously undetected in the stacked images used to derive the published
catalog made using the same dataset.
