# AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics

arXiv: https://arxiv.org/abs/2105.01016 · HTML: https://mlsanity.com/paper/2105.01016/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 6 of 6)

- [Learning to Predict the Cosmological Structure Formation](https://mlsanity.com/paper/1811.06533/) (2018)
- [Fast cosmic web simulations with generative adversarial networks](https://mlsanity.com/paper/1801.09070/) (2018)
- [Cosmological N-body simulations: a challenge for scalable generative models](https://mlsanity.com/paper/1908.05519/) (2019)
- [A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues](https://mlsanity.com/paper/1805.04537/) (2018)
- [Painting with baryons: augmenting N-body simulations with gas using deep generative models](https://mlsanity.com/paper/1903.12173/) (2019)
- [Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning](https://mlsanity.com/paper/2010.02926/) (2020)

## Built on by (most cited first, 5 of 5)

- [The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence](https://mlsanity.com/paper/2109.10915/) (2021)
- [The CAMELS project: public data release](https://mlsanity.com/paper/2201.01300/) (2022)
- [Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter](https://mlsanity.com/paper/2201.01305/) (2022)
- [Classification algorithms applied to structure formation simulations](https://mlsanity.com/paper/2106.06587/) (2021)
- [Super-resolving Dark Matter Halos using Generative Deep Learning](https://mlsanity.com/paper/2111.06393/) (2021)

## Abstract (arXiv)

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 Mpc/h, 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. We also identify and inspect dark matter halos and their substructures. Our SR model generate 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.