Astronomers spend much of their time mapping the universe, but until now their results have been collected and archived in so many different ways that the data can't talk to each other.
Each large telescope mission, project or program has its own formats, naming conventions and software tricks. And since discoveries in space science and other big-data fields increasingly come from combining data across projects, these differences may block the way forward to new science.
The Multimodal Universe (MMU) is a new effort to fix that. Astronomers from the Center for Astrophysics | Harvard & Smithsonian have led an initiative across multiple institutes to reshape more than 80 terabytes of observations, like galaxy images from radio to X-rays, spectra of stars and galaxies, and time series of flickering variable stars, into one consistent, user-friendly system.
"The idea is simple: You shouldn't need a Ph.D. in a specific survey's archival system to load the data from that survey and do cool science with it," writes CfA lead scientist Mike Smith.
With MMU, scientists and students can pull in data from many sky surveys using the same tools and formats, even on their laptops.
Publication details
Huggingface link: huggingface.co/blog/hugging-sc … imodal-universe-hats
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data, arXiv (2024). DOI: 10.48550/arxiv.2412.02527
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Citation: Astronomers build a one-stop universe data hub (2026, July 8) retrieved 13 July 2026 from https://phys.org/news/2026-07-astronomers-universe-hub.html
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