The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data
Instituto de Astrofísica de Canarias · Massachusetts Institute of Technology · University of Oxford · University of Cambridge · Space Telescope Science Institute · Stanford University · Institute of Astronomy & Kavli Institute for Cosmology, University of Cambridge · Polymathic AI / Simons Foundation · Instituto de Astrofisica De Canarias · Columbia University · Australian National University · CNRS · University of Toronto · Center for Astrophysics | Harvard & Smithsonian · Princeton University · INRIA · Flatiron Institute · University of Pennsylvania · Aspia Space · Université de Montréal
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摘要
We present the `Multimodal Universe`, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, our dataset contains hundreds of millions of astronomical observations, constituting 100TB of multi-channel and hyper-spectral images, spectra, multivariate time series, as well as a wide variety of associated scientific measurements and metadata. In addition, we include a range of benchmark tasks representative of standard practices for machine learning methods in astrophysics. This massive dataset will enable the development of large multi-modal models specifically targeted towards scientific applications. All codes used to compile the dataset, and a description of how to access the data is available at https://github.com/MultimodalUniverse/MultimodalUniverse