A First Look at Creating Mock Catalogs with Machine Learning Techniques

<p>We investigate machine learning (ML) techniques for predicting the number of galaxies (<em>N</em>_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 <em>N</em> _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 <em>k</em>-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict <em>N</em> gal by training our algorithms on the following six halo properties: number of particles, <em>M</em> ₂₀₀, σ _<em>v</em>, <em>v</em> _max, half-mass radius, and spin. For Millennium, our predicted <em>N</em> gal values have a mean-squared error (MSE) of ~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 ~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 <em>N</em> _gal. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high <em>M</em> _star, low <em>M</em> _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.