Machine-learning identification of extragalactic objects in the optical-infrared all-sky surveys

TL;DR

This paper introduces a fully-automatic machine learning model for identifying extragalactic objects in large optical-infrared sky surveys, enabling efficient classification of millions of objects.

Contribution

The authors develop a novel classification approach combining data representation, outlier detection, and hyperplane separation, applied to large-scale astronomical catalogs.

Findings

Successfully classified 38 million extragalactic objects

Achieved high accuracy in separating galaxies, quasars, and stars

Demonstrated scalability to large sky survey datasets

Abstract

We present new fully-automatic classification model to select extragalactic objects within astronomy photometric catalogs. Construction of the our classification model is based on the three important procedures: 1) data representation to create feature space; 2) building hypersurface in feature space to limit range of features (outliers detection); 3) building hyperplane separating extragalactic objects from the galactic ones. We trained our model with 1.7 million objects (1.4 million galaxies and quasars, 0.3 million stars). The application of the model is presented as a photometric catalog of 38 million extragalactic objects, identified in the WISE and Pan-STARRS catalogs cross-matched with each other.