Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate
This work provides a more efficient and accurate method for astronomers and cosmologists to estimate galaxy cluster richness, which is crucial for understanding galaxy evolution and large-scale structures, especially given the high cost of acquiring labeled astronomical data.
This paper addresses the challenge of estimating optical richness in galaxy clusters from multi-band optical images. The proposed self-supervised approach reduces the mean absolute error by 11.84% and intrinsic scatter by 20.78% compared to existing methods, while also decreasing the reliance on labeled training data by up to 60%.
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.