A Widely Applicable Galaxy Group Finder Using Machine Learning

Abstract Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for identifying groups through machine learning techniques in real space, taking into account the impact of redshift distortion. Our methodology involves two neural networks: one is a classification model for identifying central galaxy groups, and the other is a regression model for predicting the mass of these groups. Both models input observable galaxy traits, allowing future applicability to real survey data. Testing on simulated datasets indicates our method accurately identifies over 92% of groups with M vir ≥ 10 11 h −1 M ⊙ , with 80% achieving a membership completeness of at least 80%. The predicted group masses vary by less than 0.3 dex across different mass scales, even in the absence of a priori data. Our network adapts seamlessly to expand to sparse samples with a flux limit of m r < 14, to high redshift samples at z = 1.08, and to galaxy samples from the TNG300 hydrodynamical simulation without further training. Furthermore, the framework can easily adjust to real surveys by training on redshift-distorted samples without needing parameter changes. Careful consideration of different observational effects in redshift space makes it promising that this method will be applicable to real galaxy surveys.