Integrating human and machine intelligence in galaxy morphology classification tasks

TL;DR

This paper presents a hybrid system combining human and machine learning for galaxy morphology classification, significantly increasing classification speed while maintaining high accuracy, crucial for large-scale astronomical surveys.

Contribution

It introduces an integrated approach using Bayesian aggregation and Random Forest algorithms to enhance classification efficiency and accuracy in galaxy morphology tasks.

Findings

Nearly 5-fold increase in classification rate using Bayesian aggregation.

Achieved 95.7% accuracy with reprocessed GZ2 data.

At least an 8-fold increase in classification speed with combined system.

Abstract

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold, classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of nonparametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine, and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computation cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large scale surveys.