Knowledge Discovery Workflows in the Exploration of Complex Astronomical Datasets

Abstract

The massive amount of data produced by the recent multi-wavelength large-area surveys has spurred the growth of unprecedentedly massive and complex astronomical datasets that are proving the traditional data analysis techniques more and more inadequate. Knowledge discovery techniques, while relatively new to astronomy, have been successfully applied in several other quantitative disciplines for the determination of patterns in extremely complex datasets. The concerted use of different unsupervised and supervised machine learning techniques, in particular, can be a powerful approach to answer specific questions involving high-dimensional datasets and degenerate observables. In this paper I will present CLaSPS, a data-driven methodology for the discovery of patterns in high-dimensional astronomical datasets based on the combination of clustering techniques and pattern recognition algorithms. I shall also describe the result of the application of CLaSPS to a sample of a peculiar class of AGNs, the blazars.