Published September 11, 2020
| Version v2
Other
Open
Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning structures and recommendations for 2020-2050
Authors/Creators
- 1. Atmosphere and Space Technology Research Associates
- 2. NASA Goddard Space flight Center
- 3. CIRES, University of Colorado & NOAA Space Weather Prediction Center,Boulder, CO, USA
- 4. Department of Atmospheric and Oceanic Sciences, University of California at Los Angeles
- 5. Stanford University
- 6. Department of Mathematics, KULeuven, University of Leuven, Belgium
- 7. The Johns Hopkins University
- 8. University of New Hampshire
- 9. SANSA, Hermanus, South Africa
- 10. Trinity College Dublin and Dublin Institute for Advanced Studies, Ireland
- 11. CIRES CU Boulder and NOAA Space Weather Prediction Center
- 12. Predictive Science Inc.
- 13. Lockheed Martin Solar & Astrophysics Laboratory
- 14. National Solar Observatory, CU Boulder
- 15. STCE/Royal Observatory of Belgium
Description
We are at a crossroads in the study of Heliophysics. On one hand we operate in the same paradigm that has guided the field over the past couple of decades, ruled by the triumvirate of data, theory, and simulations. On the other hand, we are beginning to recognize that powerful new opportunities for scientific discovery are possible through increased data volume and sophisticated methods to explore these data. This paper focuses on an important and poorly communicated side of data science and machine learning: the application of these methods as discovery tools. We cover the progress of data science and machine learning in Heliophysics at a high level and establish the importance of the notion of explainability of models.