DOI: 10.5281/zenodo.4646764
title: Parametrized cosmological mass maps dataset

description: <strong>Parametrized cosmological mass maps dataset</strong> This dataset consists of the non-tomographic training and testing set without noise and intrinsic alignments. It was introduced in the following paper<br> Fluri, Janis, et al. "Cosmological constraints with deep learning from KiDS-450 weak lensing maps." Physical Review D 100.6 (2019): 063514.<br> Furthermore, this dataset is released with the following paper:<br> 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 <strong>Description</strong> The simulation grid consists of $57$ different cosmologies assuming a flat LambdaCDM universe. <br> 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<br> 0.101, 1.304 <br> 0.102, 1.125 <br> 0.103, 0.947 <br> 0.120, 1.178 <br> 0.123, 1.006 <br> 0.127, 0.836 <br> 0.137, 1.230 <br> 0.142, 1.063 <br> 0.148, 0.900 <br> 0.154, 1.281 <br> 0.156, 0.741 <br> 0.161, 1.119 <br> 0.169, 0.961 <br> 0.171, 1.331 <br> 0.178, 0.807 <br> 0.179, 1.173 <br> 0.188, 1.019 <br> 0.189, 0.659 <br> 0.196, 1.225 <br> 0.199, 0.870 <br> 0.207, 1.075 <br> 0.212, 0.727 <br> 0.219, 0.930 <br> 0.225, 1.129 <br> 0.227, 0.591 <br> 0.233, 0.791 <br> 0.238, 0.988 <br> 0.250, 0.658 <br> 0.254, 0.852 <br> 0.257, 1.043 <br> 0.269, 0.534 <br> 0.271, 0.723 <br> 0.273, 0.910 <br> 0.291, 0.601 <br> 0.291, 0.783 <br> 0.292, 0.966 <br> 0.311, 0.842 <br> 0.312, 0.664 <br> 0.314, 0.487 <br> 0.330, 0.898 <br> 0.332, 0.724 <br> 0.335, 0.552 <br> 0.352, 0.782 <br> 0.356, 0.614 <br> 0.370, 0.838 <br> 0.376, 0.673 <br> 0.382, 0.510 <br> 0.395, 0.730 <br> 0.402, 0.570 <br> 0.413, 0.784 <br> 0.421, 0.628 <br> 0.431, 0.475 <br> 0.440, 0.683 <br> 0.450, 0.533 <br> 0.458, 0.737 <br> 0.469, 0.589 <br> 0.487, 0.643 <br> Each zip file in the dataset corresponds to 1 of these combinations and contains 12 files containing 1000 images.<br> The source galaxy redshift distribution corresponding to these maps is the full, non-tomographic redshift distribution n(z) from Fluri et. al.<br> The projected matter distribution was pixelised into images of size 128px x 128px, which correspond to 5deg x 5deg of the sky. <br> Eventually, the resulting dataset consists of 57 sets of 12'000 sky convergence maps for a total of $684'000$ samples. <strong>Citations</strong><br> If you use this dataset, please cite: <pre><code>@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} }</code></pre> and <pre><code>@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} }</code></pre>