Context. With the growth of the scale, depth, and resolution of astronomical imaging surveys, there is increased need for highly accurate automated detection and extraction of astronomical sources from images. This also means there is a need for objective quality criteria, and automated methods to optimise parameter settings for these software

When studying the classification of galaxies with unsupervised machine learning using the physical information available in data such as the SDSS MaNGA integral field spectroscopic survey, outliers appear in the results. These may result from non-physical issues or confusions in the software, but may also constitute a potent tool to find 'strange'

We provide an updated assessment of the power of the Cherenkov Telescope Array (CTA) to search for thermally produced dark matter at the TeV scale, via the associated gamma-ray signal from pair-annihilating dark matter particles in the region around the Galactic centre. We find that CTA will open a new window of discovery potential, significantly