Star-Galaxy Image Separation with Computationally Efficient Gaussian Process Classification

TL;DR

This paper presents a scalable Gaussian process classification method using PCA embeddings for efficient star-galaxy separation in large-scale survey images, outperforming some existing algorithms and providing uncertainty quantification.

Contribution

The authors develop a linear-scaling hyperparameter training method for Gaussian processes applied to PCA-embedded image data, enabling large-scale optical image classification.

Findings

GP models perform favorably compared to CNNs and morphology discriminators.

The method provides reliable uncertainty estimates for classifications.

Scalable GP classification effectively processes large survey datasets.

Abstract

We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in high-dimensional data modalities such as optical image classification, we show that a low-dimensional embedding of images into a metric space defined by the principal components of the data suffices to produce high-quality predictions from real large-scale survey data. We develop a novel method of GP classification hyperparameter training that scales approximately linearly in the number of image observations, which allows for application of GP models to large-size Hyper Suprime-Cam (HSC) Subaru Strategic Program data. In our experiments we evaluate the performance of a principal component analysis (PCA) embedded GP predictive model against other machine learning algorithms including a convolutional neural network and an image photometric morphology discriminator. Our analysis shows that our methods compare favorably with current methods in optical image classification while producing posterior distributions from the GP regression that can be used to quantify object classification uncertainty. We further describe how classification uncertainty can be used to efficiently parse large-scale survey imaging data to produce high-confidence object catalogs.