- Solar physicists identify which features are most useful for predicting solar flares, which takes requires more data than any other satellite in NASA history.
- Researchers catalogued flaring and non-flaring regions from a database of more than 2,000 active regions and then characterized those regions by 25 features such as energy, current and field gradient. They then fed the machine-learning system 70 percent of the data,to train it to identify relevant features
- However, this study only used information from the solar surface. That would be like trying to predict Earth’s weather from only surface measurements like temperature, without considering the wind and cloud cover. The next step in solar flare prediction would be to incorporate data from the sun’s atmosphere.
Machine learning helps Stanford physicists predict dangerous solar flares earlier
1. 15. 15 by Alex Klokus