Yangyao Chen

TL;DR: Yang et al. as discussed by the authors extended the halo-based group finder to use data simultaneously with either photometric or spectroscopic redshifts, and they used a mock galaxy redshift surveys constructed from a high-resolution N-body simulation.

TL;DR: In this paper, the authors used decision trees built with the random ensemble method to study the relation of the structural properties of dark matter halos to their assembly history and environment, and found that about $60%, $10%, and $20% of the variances in halo concentration, axis ratio, and spin, respectively, can be explained by combining four dominating predictors: the first PC of the assembly history, halo mass, and two environment parameters.

TL;DR: In this paper, a method based on the conditional stellar mass functions in dark matter halos, which can provide an unbiased estimate of the global GSMF has been proposed to deal with the effects of the cosmic variance in the estimate of galaxy stellar mass function.

TL;DR: In this article, 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 is presented.