A First Look at creating mock catalogs with machine learning techniques

TL;DR

This paper explores machine learning methods, specifically SVM and kNN, to predict galaxy counts in halos for mock catalog creation, offering a non-parametric alternative to traditional models with promising accuracy.

Contribution

It introduces ML techniques for halo galaxy prediction that do not rely on prescribed relationships, improving flexibility and feature selection in mock catalog generation.

Findings

Achieved MSE of ~0.16 in galaxy number predictions.

Predicted distributions match observed halo distributions.

Galaxy correlation functions are accurate within 5-10% at large scales.

Abstract

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 2 algorithms: support vector machines (SVM) and k-nearest-neighbour (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N_gal by training our algorithms on the following 6 halo properties: number of particles, M_200, \sigma_v, v_max, half-mass radius and spin. For Millennium, our predicted N_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 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, machine learning offers an interesting alternative for creating mock catalogs.