The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data
Eirini Angeloudi, Jeroen Audenaert, Micah Bowles, Benjamin M. Boyd, David Chemaly, Brian Cherinka, Ioana Ciuca, Miles Cranmer
Abstract
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
BibTeX
@inproceedings{
angeloudi2024the,
title={The Multimodal Universe: Enabling Large-Scale Machine Learning with 100{TB} of Astronomical Scientific Data},
author={Eirini Angeloudi and Jeroen Audenaert and Micah Bowles and Benjamin M. Boyd and David Chemaly and Brian Cherinka and Ioana Ciuca and Miles Cranmer and Aaron Do and Matthew Grayling and Erin Elizabeth Hayes and Tom Hehir and Shirley Ho and Marc Huertas-Company and Kartheik G. Iyer and Maja Jablonska and Francois Lanusse and Henry W. Leung and Kaisey Mandel and Juan Rafael Mart{\'\i}nez-Galarza and Peter Melchior and Lucas Thibaut Meyer and Liam Holden Parker and Helen Qu and Jeff Shen and Michael J. Smith and Connor Stone and Mike Walmsley and John F Wu},
booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024},
url={https://openreview.net/forum?id=EWm9zR5Qy1}
}