ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection

Doorenbos, Lars Jelte; Torbaniuk, Olena; Cavuoti, Stefano; Paolillo, Maurizio; Longo, Giuseppe; Brescia, Massimo; Sznitman, Raphael; Márquez-Neila, Pablo (2022). ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection. Astronomy and astrophysics, 666, A171. EDP Sciences 10.1051/0004-6361/202243900

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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. 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. 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. 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. 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.

Item Type:

Journal Article (Original Article)


10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Doorenbos, Lars Jelte, Sznitman, Raphael, Márquez Neila, Pablo


500 Science > 520 Astronomy




EDP Sciences




Lars Jelte Doorenbos

Date Deposited:

14 Sep 2022 14:27

Last Modified:

05 Dec 2022 16:23

Publisher DOI:






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