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CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

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Published in:Computational Astrophysics and Cosmology
Format: Online Article RSS Article
Published: 2019
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container_title Computational Astrophysics and Cosmology
description
discipline_display Natural Sciences
discipline_facet Natural Sciences
format Online Article
RSS Article
genre Journal Article
id rss_article:21821
institution FRELIP
journal_source_facet Computational Astrophysics and Cosmology
publishDate 2019
publishDateSort 2019
record_format rss_article
spellingShingle CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
Astronomy
Natural Sciences — Physical Sciences
Natural Sciences
sub_discipline_display Natural Sciences — Physical Sciences
sub_discipline_facet Natural Sciences — Physical Sciences
subject_display Astronomy
Natural Sciences — Physical Sciences
Natural Sciences
Astronomy
Natural Sciences — Physical Sciences
Natural Sciences
subject_facet Astronomy
Natural Sciences — Physical Sciences
Natural Sciences
title CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
title_auth CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
title_full CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
title_fullStr CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
title_full_unstemmed CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
title_short CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
title_sort cosmogan: creating high-fidelity weak lensing convergence maps using generative adversarial networks
topic Astronomy
Natural Sciences — Physical Sciences
Natural Sciences
url https://link.springer.com/article/10.1186/s40668-019-0029-9