Smith42/astroddpm
A denoising diffusion probabilistic model synthesises galaxies that are qualitatively and physically indistinguishable from the real thing.
This tool helps astrophysicists and astronomers generate realistic galaxy images that are physically indistinguishable from real ones. It takes existing galaxy image datasets as input and produces new, synthetic galaxy images. Researchers studying galaxy formation, cosmic evolution, or training machine learning models on astronomical data would use this.
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Use this if you need to create large numbers of synthetic galaxy images for research, simulations, or to augment datasets when real observations are limited.
Not ideal if you are looking for tools to analyze real-world observational data directly or to process telescope images without synthetic generation.
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AGPL-3.0
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Last pushed
Mar 26, 2022
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