The Multimodal Universe compiles hundreds of millions of astronomical observations from surveys such as DESI, Gaia and JWST into a unified 100 TB multimodal dataset for machine learning.
A New Task: Deriving Semantic Class Targets for the Physical Sciences
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abstract
We define deriving semantic class targets as a novel multi-modal task. By doing so, we aim to improve classification schemes in the physical sciences which can be severely abstracted and obfuscating. We address this task for upcoming radio astronomy surveys and present the derived semantic radio galaxy morphology class targets.
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The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe compiles hundreds of millions of astronomical observations from surveys such as DESI, Gaia and JWST into a unified 100 TB multimodal dataset for machine learning.