The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data
作者: Eirini Angeloudi, Jeroen Audenaert, Micah Bowles, Benjamin M. Boyd, David Chemaly, Brian Cherinka, Ioana Ciuca, Miles Cranmer, Aaron Do, Matthew Grayling, Erin Elizabeth Hayes, Tom Hehir, Shirley Ho, Marc Huertas-Company, Kartheik G. Iyer, Maja Jablonska, Francois Lanusse, Henry W. Leung, Kaisey Mandel, Juan Rafael Martínez-Galarza, Peter Melchior, Lucas Thibaut Meyer, Liam Holden Parker, Helen Qu, Jeff Shen, Michael J. Smith, Connor Stone, Mike Walmsley, John F Wu
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