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Document type:
Forschungsdaten 
Publication date:
12.07.2022 
Authors:
Benjamin J. Holzschuh; Conor M. O’Riordan; Simona Vegetti; Vicente Rodriguez-Gomez; Nils Thuerey 
Author affiliation:
TUM 
Publisher:
TUM 
Title:
AI Generated Galaxy Images 
Time of production:
24.03.2022 
Subject area:
DAT Datenverarbeitung, Informatik; PHY Physik 
Other subject areas:
Astronomy, Astrophysics, Artificial Intelligence, Image processing, Computer Vision 
Resource type:
Simulationen / simulations; Abbildungen von Objekten / image of objects 
Data type:
Bilder / images; Datenbanken / data bases 
Description:
Datasets of AI generated galaxy images based on simulations (TNG-Illustris), observations (COSMOS) and analytic expressions (Sérsic profiles) 
Method of data assessment:
Recent generative models were trained on datasets of galaxy images. The available datasets consist of galaxy images sampled from a StyleGAN-like model. 
Key words:
Machine Learning, Astrophysics, Cosmology 
Technical remarks:
View and download (51 GB total, 4 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1661654):
rsync rsync://m1661654@dataserv.ub.tum.de/m1661654/ 
Language:
en 
Rights:
by, http://creativecommons.org/licenses/by/4.0 
Horizon 2020:
ERC Consolidator Grant SpaTe (CoG-2019-863850), European Union’s Horizon 2020 research and innovation pro-gramme (LEDA: grant agree-ment no. 758853)