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DMSR-WGAN: Dark-Matter Super-Resolution WGAN

This repository contains code and resources aimed at enhancing the particle resolution of dark matter-only simulations using a Wasserstein Generative Adversarial Network (WGAN). This work has applications in astrophysics and cosmology, particularly for improving simulation fidelity in large-scale structure studies.

Table of Contents

  1. Project Overview
  2. Results

Project Overview

The DMSR-WGAN uses adversarial training to super-resolve dark matter simulations. It generates high-resolution particle distributions from low-resolution inputs. This approach enables:

  • Enhanced particle resolution.
  • Improved substructure identification.
  • Reduced computational cost compared to high-resolution simulations.

For more details, refer to: On the Use of WGANs for Super Resolution in Dark-Matter Simulations.

Results

For scripts and steps to reproduce results from On the Use of WGANs for Super Resolution in Dark-Matter Simulations, please switch to the on-wgan-super-resolution branch.