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

Conference · Thu Dec 03 04:00:00 EST 2020

OSTI ID:1887831

Liang, Gongbo ^[1]; Su, Yuanyuan ^[2]; Lin, Sheng-Chieh ^[2]; Zhang, Yu ^[2]; Zhang, Yuanyuan ^[3]; Jacobs, Nathan ^[2]

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1. Kentucky U.; Eastern Kentucky U.
2. Kentucky U.
3. Fermilab

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.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 1887831
• Report Number(s): FERMILAB-CONF-20-798-V; arXiv:2012.02368; oai:inspirehep.net:1835117
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS