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 tools. Aims. We present a comparison of several tools developed to perform this task: namely SExtractor, ProFound, NoiseChisel, and MTObjects. In particular, we focus on evaluating performance in situations that present challenges for detection. For example, faint and diffuse galaxies; extended structures, such as streams; and objects close to bright sources. Furthermore, we develop an automated method to optimise the parameters for the above tools. Methods. We present four different objective segmentation quality measures, based on precision, recall, and a new measure for the correctly identified area of sources. Bayesian optimisation is used to find optimal parameter settings for each of the four tools when applied to simulated data, for which a ground truth is known. After training, the tools are tested on similar simulated data in order to provide a performance baseline. We then qualitatively assess tool performance on real astronomical images from two different surveys. Results. We determine that when area is disregarded, all four tools are capable of broadly similar levels of detection completeness, while only NoiseChisel and MTObjects are capable of locating the faint outskirts of objects. MTObjects achieves the highest scores on all tests for all four quality measures, whilst SExtractor obtains the highest speeds. No tool has sufficient speed and accuracy to be well suited to large-scale automated segmentation in its current form.
Context. Modern sky surveys are producing ever larger amounts of observational data, which makes the application of classical approaches for the classification and analysis of objects challenging and time-consuming. However, this issue may be significantly mitigated by the application of automatic machine and deep learning methods. Aims. We propose ULISSE, a new deep learning tool that, starting from a single prototype object, is capable of identifying objects sharing the same morphological and photometric properties, and hence of creating a list of candidate sosia. In this work, we focus on applying our method to the detection of AGN candidates in a Sloan Digital Sky Survey galaxy sample, since the identification and classification of Active Galactic Nuclei (AGN) in the optical band still remains a challenging task in extragalactic astronomy. Methods. Intended for the initial exploration of large sky surveys, ULISSE directly uses features extracted from the ImageNet dataset to perform a similarity search. The method is capable of rapidly identifying a list of candidates, starting from only a single image of a given prototype, without the need for any time-consuming neural network training. Results. Our experiments show ULISSE is able to identify AGN candidates based on a combination of host galaxy morphology, color and the presence of a central nuclear source, with a retrieval efficiency ranging from 21 % to 65 % (including composite sources) depending on the prototype, where the random guess baseline is 12 %. We find ULISSE to be most effective in retrieving AGN in early-type host galaxies, as opposed to prototypes with spiral-or late-type properties. Conclusions. Based on the results described in this work, ULISSE can be a promising tool for selecting different types of astrophysical objects in current and future wide-field surveys (e.g. Euclid, LSST etc.) that target millions of sources every single night.
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