Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 499, Issue 1, Pages 89-105Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/staa2816
Keywords
methods: statistical; galaxies: groups: general; dark matter; large-scale structure of Universe
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Funding
- National Key Research and Development Program of China [2018YFA0404502, 2018YFA0404503]
- National Science Foundation of China [11821303, 11973030, 11673015, 11733004, 11761131004, 11761141012]
- ESO Telescopes at the La Silla Paranal Observatory [179.A-2005]
- China Scholarship Council
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Identifying galaxy groups from redshift surveys of galaxies plays an important role in connecting galaxies with the underlying dark matter distribution. Current and future high-z spectroscopic surveys, usually incomplete in redshift sampling, present both opportunities and challenges to identifying groups in the high-z Universe. We develop a group finder that is based on incomplete redshift samples combined with photometric data, using a machine learning method to assign halo masses to identified groups. Test using realistic mock catalogues shows that greater than or similar to 90 per cent of true groups with halomasses M-h greater than or similar to 10(12)M(circle dot) h(-1) are successfully identified, and that the fraction of contaminants is smaller than 10 per cent. The standard deviation in the halo mass estimation is smaller than 0.25 dex at all masses. We apply our group finder to zCOSMOS-bright and describe basic properties of the group catalogue obtained.
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