A First Look at Creating Mock Catalogs with Machine Learning Techniques

We investigate machine learning (ML) techniques for predicting the number of galaxies (N-gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N-gal. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N-gal by training our algorithms on the following six halo properties: number of particles, M-200, sigma(v), v(max), half-mass radius, and spin. For Millennium, our predicted Ngal values have a mean-squared error (MSE) of similar to 0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to similar to 5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N-gal. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e. g., blue, red, high M-star, low M-star). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs.