Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050
Instrumentation and Methods for Astrophysics
2022-12-29 v1 Solar and Stellar Astrophysics
Artificial Intelligence
Machine Learning
Abstract
Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.
Keywords
Cite
@article{arxiv.2212.13325,
title = {Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050},
author = {R. M. McGranaghan and B. Thompson and E. Camporeale and J. Bortnik and M. Bobra and G. Lapenta and S. Wing and B. Poduval and S. Lotz and S. Murray and M. Kirk and T. Y. Chen and H. M. Bain and P. Riley and B. Tremblay and M. Cheung and V. Delouille},
journal= {arXiv preprint arXiv:2212.13325},
year = {2022}
}
Comments
4 pages; Heliophysics 2050 White Paper