A sparse regression approach to modeling the relation between galaxy stellar masses and their host halos

Understand

Sparse regression algorithms have been proposed as the appropriate framework to model the governing equations of a system from data, without needing prior knowledge of the underlying physics.

• In this work, we use sparse regression to build an accurate and explainable model of the stellar mass of central galaxies given properties of their host dark matter (DM) halo.
• Our data set comprises 9,521 central galaxies from the EAGLE hydrodynamic simulation.
• By matching the host halos to a DM-only simulation, we collect the halo mass and specific angular momentum at present time and for their main progenitors in 10 redshift bins from $z=0$ to $z=4$.