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Identifying galaxy groups from redshift surveys of galaxies plays animportant role in connecting galaxies with the underlying dark matterdistribution. Current and future high-$z$ spectroscopic surveys, usuallyincomplete in redshift sampling, present both opportunities and challenges toidentifying groups in the high-$z$ Universe. We develop a group finder that isbased on incomplete redshift samples combined with photometric data, using amachine learning method to assign halo masses to identified groups. Test usingrealistic mock catalogs shows that $\gtrsim 90\%$ of true groups with halomasses $\rm M_h \gtrsim 10^{12} M_{\odot}/h$ are successfully identified, andthat the fraction of contaminants is smaller than $10\%$. The standarddeviation 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 ofthe group catalog obtained.

Identifying galaxy groups at high redshift from incomplete spectroscopic data: I. The group finder and application to zCOSMOS