A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey

Tsung Chi Chen, Yen Ting Lin, Hsi Yu Schive, Masamune Oguri, Kai Feng Chen, Nobuhiro Okabe, Sadman Ali, Connor Bottrell, Roohi Dalal, Yusei Koyama, Rogério Monteiro-Oliveira, Rhythm Shimakawa, Tomotsugu Goto, Bau Ching Hsieh, Tadayuki Kodama, Atsushi J. Nishizawa

Research output: Contribution to journalArticlepeer-review

Abstract

Superclusters, encompassing environments across a wide range of overdensities, can be regarded as unique laboratories for studying galaxy evolution. Although numerous supercluster catalogs have been published, none of them goes beyond redshift z = 0.7. In this work, we adopt a physically motivated supercluster definition, requiring that superclusters should eventually collapse even in the presence of dark energy. Applying a friends-of-friends (FoF) algorithm to the CAMIRA cluster sample constructed using the Subaru Hyper Suprime-Cam survey data, we have conducted the first systematic search for superclusters at z = 0.5-1.0 and identified 673 supercluster candidates over an area of 1027 deg2. The FoF algorithm is calibrated by evolving N-body simulations to the far future to ensure high purity. We found that these high-z superclusters are mainly composed of two to four clusters, suggesting the limit of gravitationally bound structures in the younger Universe. In addition, we studied the properties of the clusters and brightest cluster galaxies (BCGs) residing in different large-scale environments. We found that clusters associated with superclusters are typically richer, but no apparent dependence of the BCG properties on large-scale structures is found. We also compared the abundance of observed superclusters with mock superclusters extracted from halo light cones, finding that photometric redshift uncertainty is a limiting factor in the performance of superclusters detection.

Original languageEnglish
Article number200
Number of pages28
JournalAstrophysical Journal
Volume975
Issue number2
Early online date4 Nov 2024
DOIs
Publication statusPublished - 10 Nov 2024

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