no code implementations • 7 Feb 2019 • Victor F. Calderon, Andreas A. Berlind
We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural network) to predict halo masses using a set of synthetic galaxy catalogues that are built by populating dark matter haloes in N-body simulations with galaxies, and that match both the clustering and the joint-distributions of properties of galaxies in the Sloan Digital Sky Survey (SDSS).
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
