AI-assisted superresolution cosmological simulations – II. Halo substructures, velocities, and higher order statistics

Abstract

In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations. The SR model enhances the simulation resolution by generating 512 times more tracer particles, extending into the deeply non-linear regime where complex structure formation processes take place. We validate the SR model by deploying the model in 10 test simulations of box size 100 h−1 Mpc, and examine the matter power spectra, bispectra and 2D power spectra in redshift space. We find the generated SR field matches the true HR result at percent level down to scales of k ∼ 10 h Mpc−1. We also identify and inspect dark matter haloes and their substructures. Our SR model generates visually authentic small-scale structures, that cannot be resolved by the LR input, and are in good statistical agreement with the real HR results. The SR model performs satisfactorily on the halo occupation distribution, halo correlations in both real and redshift space, and the pairwise velocity distribution, matching the HR results with comparable scatter, thus demonstrating its potential in making mock halo catalogs. The SR technique can be a powerful and promising tool for modelling small-scale galaxy formation physics in large cosmological volumes.

Large-scale dark matter simulations

“…The architectures used to create fake data can also be combined with existing data from, for instance, low resolution simulations to artificially increase their resolution. This task, usually referred to as ''super resolution'' has been performed by e.g., KodiRamanah et al (2020), Li et al (2020) where small-scale high-resolution features were 'in-painted' onto low-resolution N-body simulations, finding good agreement for a selection of density, velocity, and (sub)halo statistics (Ni et al 2021).…”

Machine Learning and Cosmology

“…Traditional simulations, or parts of them, can be accelerated with ML [83][84][85]. High-resolution simulations may be emulated based on low-resolution ones [86][87][88]; expensive physical calculations replaced with a machine learning interpolation thereof [82]. This may increase the slope of the relation between computational resources and the size and resolution of the simulations.…”

Large-scale dark matter simulations

“…The architectures used to create fake data can also be combined with existing data from, for instance, low resolution simulations to artificially increase their resolution. This task, usually referred to as "super resolution" has been performed by e.g., KodiRamanah et al (2020); Li et al (2020) where small-scale high-resolution features were 'in-painted' onto low-resolution N -body simulations, finding good agreement for a selection of density, velocity, and (sub)halo statistics (Ni et al 2021).…”