# Painting halos from cosmic density fields of dark matter with physically motivated neural networks

## TL;DR

This paper introduces a physically motivated neural network that efficiently maps dark matter density fields to realistic halo distributions, enabling fast and scalable generation of 3D halo maps without full simulations.

## Contribution

The authors develop a novel neural network model that learns local relations between dark matter and halos using minimal data and physical priors, bypassing complex simulations.

## Key findings

- Accurately reproduces power spectrum and bispectrum statistics.
- Can predict large-scale halo distributions from small simulation training.
- Operates efficiently with local patches, enabling arbitrary simulation sizes.

## Abstract

We present a novel halo painting network that learns to map approximate 3D dark matter fields to realistic halo distributions. This map is provided via a physically motivated network with which we can learn the non-trivial local relation between dark matter density field and halo distributions without relying on a physical model. Unlike other generative or regressive models, a well motivated prior and simple physical principles allow us to train the mapping network quickly and with relatively little data. In learning to paint halo distributions from computationally cheap, analytical and non-linear density fields, we bypass the need for full particle mesh simulations and halo finding algorithms. Furthermore, by design, our halo painting network needs only local patches of dark matter density to predict the halos, and as such, it can predict the 3D halo distribution for any arbitrary simulation box size. Our neural network can be trained using small simulations and used to predict large halo distributions, as long as the resolutions are equivalent. We evaluate our model's ability to generate 3D halo count distributions which reproduce, to a high degree, summary statistics such as the power spectrum and bispectrum, of the input or reference realizations.