Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate

TL;DR

This paper introduces a self-supervised learning method to estimate galaxy cluster richness from multi-band optical images, significantly reducing labeled data requirements and improving accuracy in astronomical analysis.

Contribution

A novel self-supervised approach for optical richness estimation that leverages unlabeled data, reducing label dependency and enhancing prediction accuracy.

Findings

Reduced mean absolute error by 11.84%

Lowered intrinsic scatter by 20.78%

Decreased labeled data needs by up to 60%

Abstract

Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding the co-evolution of galaxies and large-scale structures in modern astronomy and cosmology. The determination of optical richness can be challenging. We propose a self-supervised approach for estimating optical richness from multi-band optical images. The method uses the data properties of the multi-band optical images for pre-training, which enables learning feature representations from a large but unlabeled dataset. We apply the proposed method to the Sloan Digital Sky Survey. The result shows our estimate of optical richness lowers the mean absolute error and intrinsic scatter by 11.84% and 20.78%, respectively, while reducing the need for labeled training data by up to 60%. We believe the proposed method will benefit astronomy and cosmology, where a large number of unlabeled multi-band images are available, but acquiring image labels is costly.