Square Kilometre Array: Mock-observations via generative deep learning
Description
In this project we use generative deep learning methods (GANs and VAEs) to produce realistic astronomical mock-observations of numerically simulated astrophysical objects, as they will be obseved by the Square Kilometre Array Telescope (SKA). This project contributes to the Swiss-wide activities for SKA of the SKACH Consortium.
Key Data
Projectlead
Deputy Projectlead
Project team
Project partners
Ecole polytechnique fédérale de Lausanne EPFL; Universität Zürich; Eidgenössische Technische Hochschule Zürich ETH; Fachhochschule Nordwestschweiz FHNW; Universität Basel; Haute école spécialisée de Suisse occidentale HES-SO
Project status
completed, 09/2021 - 12/2024
Funding partner
Staatssekretariat für Bildung, Forschung und Innovation SBFI
Project budget
240'000 CHF
Further documents and links
Publications
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Deep learning approach for identification of HII regions during reionization in 21-cm observations : III. image recovery
2024 Bianco, Michele; Giri, Sambit. K.; Sharma, Rohit; Chen, Tianyue; Krishna, Shreyam Parth; Finlay, Chris; Nistane, Viraj; Denzel, Philipp; De Santis, Massimo; Ghorbel, Hatem
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Map-to-map translation for SKA mock observations and cosmological simulations
2023 Denzel, Philipp; Schilling, Frank-Peter; Gavagnin, Elena
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Deep learning the SKA : the Square Kilometer Array project
2023 Denzel, Philipp Benedikt; Schilling, Frank-Peter; Gavagnin, Elena
