no code implementations • 11 Aug 2020 • Abigail Petulante, Andreas A. Berlind, J. Kelly Holley-Bockelmann, Manodeep Sinha
The evolution of a dark matter halo in a dark matter only simulation is governed purely byNewtonian gravity, making a clean testbed to determine what halo properties drive its fate. Using machine learning, we predict the survival, mass loss, final position, and merging time of subhalos within a cosmological N-body simulation, focusing on what instantaneous initial features of the halo, interaction, and environment matter most.
Astrophysics of Galaxies
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
no code implementations • 30 Aug 2004 • Zheng Zheng, Andreas A. Berlind, David H. Weinberg, Andrew J. Benson, Carlton M. Baugh, Shaun Cole, Romeel Dave, Carlos S. Frenk, Neal Katz, Cedric G. Lacey
In agreement with earlier results for dark matter subhalos, we find that the mean occupation function <N> for galaxies above a baryonic mass threshold can be approximated by a step function for central galaxies plus a power law for satellites, and that the distribution of satellite numbers is close to Poisson at fixed halo mass.
astro-ph
