Automatic classification of sources in large astronomical catalogs

The Vipers Team ,Agnieszka Pollo,Aleksandra Solarz,Maciej Bilicki,Tomasz Krakowski,Małgorzata Siudek,Katarzyna Małek,Tsutomu Takeuchi

doi:10.1017/s1743921319002576

Abstract

AbstractIn 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.

Highlights

Astronomy is dealing with increasingly larger data samples, and even bigger data, like those from the Large Synoptic Survey Telescope (LSST), are coming soon
Such data open new possibilities: both for discoveries of previously unknown rare classes of objects and for understanding the global properties of the known sources, as they provide samples allowing for much more refined statistical treatment. This new situation on the market of astronomical data, coupled with increasing capabilities of modern computers, resulted in a boost of different machine learning and data mining methods applied in the field of astronomy over the last few years
Machine learning based classification methods can be divided into two main families: supervised methods, where we know a priori what sources we expect to find and we use some known datasets to train algorithms to look for them, and unsupervised methods which look for separate clusters or groups in the data based on similarity of their properties in a given feature space

Summary

Introduction

Astronomy is dealing with increasingly larger data samples, and even bigger data, like those from the Large Synoptic Survey Telescope (LSST), are coming soon Such data open new possibilities: both for discoveries of previously unknown rare classes of objects and for understanding the global properties of the known sources, as they provide samples allowing for much more refined statistical treatment. This new situation on the market of astronomical data, coupled with increasing capabilities of modern computers, resulted in a boost of different machine learning and data mining methods applied in the field of astronomy over the last few years. Unsupervised machine learning techniques to search for new unknown classes of sources and new classification schemes for known (and unknown) sources

Supervised methods in search for novel sources

Unsupervised methods in search for new classification schemes

Summary