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

Abstract

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.

Accepted for publication in A&A

References in corpus (21)

• Deep Learning in Neural Networks: An Overview
• The Host Galaxies and Classification of Active Galactic Nuclei
• The Co-Evolution of Galaxies and Supermassive Black Holes: Insights from Surveys of the Contemporary Universe
• The Sloan Digital Sky Survey Quasar Catalog IV. Fifth Data Release
• Photometric redshift estimation via deep learning
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• The weirdest SDSS galaxies: results from an outlier detection algorithm
• X-ray Detected AGN in SDSS Dwarf Galaxies
• A Catalog of AGN Host Galaxies Observed with HST/ACS: Correlations between Star Formation and AGN Activity
• Star Formation Rates for photometric samples of galaxies using machine learning methods
• AGN Feedback models: Correlations with star formation and observational implications of time evolution
• Improving the reliability of photometric redshift with machine learning
• The QUEST-La Silla AGN Variability Survey: selection of AGN candidates through optical variability
• Random Forests as a viable method to select and discover high redshift quasars
• The connection between star formation and supermassive Black Hole activity in the local Universe
• A random forest-based selection of optically variable AGN in the VST-COSMOS field
• Unsupervised feature-learning for galaxy SEDs with denoising autoencoders
• Classification of Planetary Nebulae through Deep Transfer Learning
• Massive young stellar objects in the Local Group irregular galaxy NGC6822 identified using machine learning
• Implementing Dust Shielding as a Criteria for Star Formation
• Classifying Seyfert galaxies with deep learning