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.'
author:
- 'Xiaoying Xu, Shirley Ho, Hy Trac, Jeff Schneider, Barnabas Poczos , Michelle Ntampaka'
title: A First Look at creating mock catalogs with machine learning techniques
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Introduction {#sec:intro}
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As we enter the era of large-scale structure experiments such as LSST, WFIRST and Euclid, the creation of reliable mock galaxy catalogs will become increasingly more important. Such catalogs are essential for correctly characterizing the expected errors in the analyses of these datasets, calibrating analysis pipelines and ultimately measuring cosmological parameters (such as the dark energy equation of state) from galaxy clustering (e.g. @Aea12). Making mock catalogs for different subpopulations of galaxies (e.g. blue versus red, high $M_{star}$ versus low $M_{star}$, etc.) to study their clustering properties is also of utmost importance for understanding galaxy formation and evolution (e.g. @Cea08 [@Gea12]). Although these mock catalogs can be generated relatively quickly using perturbation theory-based approaches such as that described in 