A Hybrid Ensemble Learning Approach to Star-Galaxy Classification

TL;DR

This paper introduces a hybrid ensemble framework that combines multiple star-galaxy classification techniques, including morphological, machine learning, and Bayesian methods, to enhance classification accuracy in astronomical surveys.

Contribution

It presents a novel meta-classification approach that integrates diverse classifiers, demonstrating improved performance over individual methods using CFHTLenS survey data.

Findings

Bayesian combination improves overall classification accuracy

Hybrid approach outperforms individual classifiers in various scenarios

Effective even with low-quality training data

Abstract

There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a more robust star-galaxy classification. To demonstrate this hybrid, ensemble approach, we combine a purely morphological classifier, a supervised machine learning method based on random forest, an unsupervised machine learning method based on self-organizing maps, and a hierarchical Bayesian template fitting method. Using data from the CFHTLenS survey, we consider different scenarios: when a high-quality training set is available with spectroscopic labels from DEEP2, SDSS, VIPERS, and VVDS, and when the demographics of sources in a low-quality training set do not match the demographics of objects in the test data set. We demonstrate that our Bayesian combination technique improves the overall performance over any individual classification method in these scenarios. Thus, strategies that combine the predictions of different classifiers may prove to be optimal in currently ongoing and forthcoming photometric surveys, such as the Dark Energy Survey and the Large Synoptic Survey Telescope.