Preparation for CSST: Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

TL;DR

This paper introduces a rotationally invariant supervised machine learning approach using GoogLeNet for star-galaxy classification, achieving high accuracy and demonstrating the effectiveness of preprocessing techniques for future space telescope data.

Contribution

The study presents a novel preprocessing pipeline combined with GoogLeNet for star-galaxy classification, improving accuracy and preparing for CSST data analysis.

Findings

Achieved over 99.8% accuracy on COSMOS and CSST simulated data.

Preprocessing improves classification accuracy by 2-6%.

Demonstrated effectiveness of a rotationally invariant method for astronomical image classification.

Abstract

Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically classify stars and galaxies in the COSMOS field. Unlike traditional machine learning methods, we introduce several preprocessing techniques, including noise reduction and the unwrapping of denoised images in polar coordinates, applied to our carefully selected samples of stars and galaxies. By dividing the selected samples into training and validation sets in an 8:2 ratio, we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies. The results indicate that the GoogLeNet model is highly effective, achieving accuracies of 99.6% and 99.9% for stars and galaxies, respectively. Furthermore, by comparing the results with and without preprocessing, we find that preprocessing can significantly improve classification accuracy (by approximately 2.0% to 6.0%) when the images are rotated. In preparation for the future launch of the China Space Station Telescope (CSST), we also evaluate the performance of the GoogLeNet model on the CSST simulation data. These results demonstrate a high level of accuracy (approximately 99.8%), indicating that this model can be effectively utilized for future observations with the CSST.