Automatic classification of sources in large astronomical catalogs. Issue Volume 15:Issue S341(2019) (November 2019) - Pollo, Agnieszka; Solarz, Aleksandra; Siudek, Małgorzata; Małek, Katarzyna; Bilicki, Maciej; Krakowski, Tomasz; Takeuchi, Tsutomu; ,
Abstract: In this paper we address two questions related to data analysis in large astronomical datasets, and we demonstrate how they can be answered making use of machine learning techniques. The first question is: how to efficiently find previously unknown or rare objects which can be expected to exist in big data samples? Using the largest existing extragalactic all-sky survey, provided by the WISE satellite, we demonstrate that, surprisingly, supervised classification methods can come to aid. The second question is: having a sufficiently large data sample, how can we look for new optimal classification schemes, possibly finding new and previously unknown classes and subclasses of sources? Based on the VIPERS cutting-edge galaxy catalog at redshift z > 0.5, we demonstrate that unsupervised classification methods can give unexpected but physically well-motivated results.