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

TL;DR

This paper introduces a machine learning-based method for identifying galaxy groups in high-redshift spectroscopic surveys with incomplete data, achieving high accuracy and low contamination in mock tests, and applies it to the zCOSMOS survey.

Contribution

A novel group finder combining incomplete spectroscopic and photometric data with machine learning for halo mass assignment, validated with realistic mocks.

Findings

Over 90% of true groups with halo mass > 10^{12} M_sun/h are identified.

Contamination rate is below 10%.

Halo mass estimation has a standard deviation under 0.25 dex.

Abstract

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 catalogs shows that $\gtrsim 90\%$ of true groups with halo masses $\rm M_h \gtrsim 10^{12} M_{\odot}/h$ are successfully identified, and that the fraction of contaminants is smaller than $10\%$. 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 catalog obtained.