Year: 2020
Type: article
Abstract: ABSTRACT With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging data sets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect ${\sim}90{{\ \rm per\ cent}}$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models. The code used in this is available at https://github.com/carlamb/astronomical-outliers-WGAN.
Institutions: Centre National de la Recherche Scientifique, Université Paris Sciences et Lettres, Observatoire de Paris, Sorbonne Université, Laboratoire d’Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères, Université Pierre-et-Marie-Curie, University of Pennsylvania, Universidad de La Laguna, Institut d'Astrophysique de Paris, Instituto de Astrofísica de Canarias, New York University, University of Southampton
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W3012556652