An interpretable machine-learning framework for dark matter halo formation
An interpretable machine-learning framework for dark matter halo formation
ABSTRACT We present a generalization of our recently proposed machine-learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the formation of haloes over the mass range 11.4 ≤ log (M/M⊙) ≤ 13.4. The …