Generative modelling for mass-mapping with fast uncertainty quantification [arXiv]

MMGAN is a novel mass-mapping method based on the regularised conditional generative adversarial network (GAN) framework by Bendel et al.. Designed to quickly generate approximate posterior samples of the convergence field from shear data, MMGAN offers a fully data-driven approach to mass-mapping. These posterior samples allow for the creation of detailed convergence map reconstructions with associated uncertainty maps, making MMGAN a cutting-edge tool for cosmological analysis.

Installation

After cloning the repository, if in a computing cluster, first run:

source /share/apps/anaconda/3-2022.05/etc/profile.d/conda.sh

To install the conda dependencies setting the correct channels:

conda create --name cGAN --file conda_requirements.txt --channel pytorch --channel nvidia --channel conda-forge --channel defaults

Then activate the conda environment and install the pip requirements:

conda activate cGAN
pip install -r pypi_requirements.txt

Reproducing our Results

See docs/mass_mapping.md for detailed instructions on how to setup and reproduce the results from our paper on MMGAN.

Alternatively, we have provided a [zenodo file]https://zenodo.org/records/14226221 with the weights of our trained model, as well as a number of simulations.

Questions and Concerns

If you have any questions, or run into any issues, don't hesitate to reach out at jessica.whitney.22@ucl.ac.uk

References

This repository was forked from rcGAN by Bendel et al., with significant changes and modification made by Whitney et al.

Citation

If you find this code helpful, please cite our paper:

@journal{2024arxiv,
  author = {Whitney, Jessica and Liaudat, Tobías and Price, Matthew and Mars, Matthijs and McEwen, Jason},
  title = {Generative modelling for mass-mapping with fast uncertainty quantification},
  year = {2024},
  journal={arXiv:2410.24197}
}