Parametrized cosmological mass maps dataset

Parametrized cosmological mass maps dataset

This dataset consists of the non-tomographic training and testing set without noise and intrinsic alignments. 

It was introduced in the following paper
Fluri, Janis, et al. "Cosmological constraints with deep learning from KiDS-450 weak lensing maps." Physical Review D 100.6 (2019): 063514.
Furthermore, this dataset is released with the following paper:
Perraudin, Nathanaël, et al. "Emulation of cosmological mass maps with conditional generative adversarial networks." arXiv preprint arXiv:2004.08139 (2020).

Code related to this dataset can be found in https://renkulab.io/projects/nathanael.perraudin/darkmattergan

Description

The simulation grid consists of $57$ different cosmologies assuming a flat LambdaCDM universe. 
Each of these 57 configurations was run with different values of Omega_m and sigma_8, resulting in the following parameter grid.| Omega_m, sigma_8
   0.101, 1.304 
   0.102, 1.125 
   0.103, 0.947 
   0.120, 1.178 
   0.123, 1.006 
   0.127, 0.836 
   0.137, 1.230 
   0.142, 1.063 
   0.148, 0.900 
   0.154, 1.281 
   0.156, 0.741 
   0.161, 1.119 
   0.169, 0.961 
   0.171, 1.331 
   0.178, 0.807 
   0.179, 1.173 
   0.188, 1.019 
   0.189, 0.659 
   0.196, 1.225 
   0.199, 0.870 
   0.207, 1.075 
   0.212, 0.727 
   0.219, 0.930 
   0.225, 1.129 
   0.227, 0.591 
   0.233, 0.791 
   0.238, 0.988 
   0.250, 0.658 
   0.254, 0.852 
   0.257, 1.043 
   0.269, 0.534 
   0.271, 0.723 
   0.273, 0.910 
   0.291, 0.601 
   0.291, 0.783 
   0.292, 0.966 
   0.311, 0.842 
   0.312, 0.664 
   0.314, 0.487 
   0.330, 0.898 
   0.332, 0.724 
   0.335, 0.552 
   0.352, 0.782 
   0.356, 0.614 
   0.370, 0.838 
   0.376, 0.673 
   0.382, 0.510 
   0.395, 0.730 
   0.402, 0.570 
   0.413, 0.784 
   0.421, 0.628 
   0.431, 0.475 
   0.440, 0.683 
   0.450, 0.533 
   0.458, 0.737 
   0.469, 0.589 
   0.487, 0.643 
 

Each zip file in the dataset corresponds to 1 of these combinations and contains 12 files containing 1000 images.
The source galaxy redshift distribution corresponding to these maps is the full, non-tomographic redshift distribution n(z) from Fluri et. al.
The projected matter distribution was pixelised into images of size 128px x 128px, which correspond to 5deg x 5deg of the sky. 
Eventually, the resulting dataset consists of 57 sets of 12'000 sky convergence maps for a total of $684'000$ samples. 

Citations
If you use this dataset, please cite:

@article{perraudin2020emulation,
  title={Emulation of cosmological mass maps with conditional generative adversarial networks},
  author={Perraudin, Nathana{\"e}l and Marcon, Sandro and Lucchi, Aurelien and Kacprzak, Tomasz},
  journal={arXiv preprint arXiv:2004.08139},
  year={2020}
}

and

@article{fluri2019cosmological,
  title={Cosmological constraints with deep learning from KiDS-450 weak lensing maps},
  author={Fluri, Janis and Kacprzak, Tomasz and Lucchi, Aurelien and Refregier, Alexandre and Amara, Adam and Hofmann, Thomas and Schneider, Aurel},
  journal={Physical Review D},
  volume={100},
  number={6},
  pages={063514},
  year={2019},
  publisher={APS}
}