FRANCE: Physicists from Stanford University have applied a machine learning algorithm to better forecast solar flares that could expose high-flying airline passengers to radiation and disrupt power grids and communication satellites.
Physicists Monica Bobra and Sebastien Couvidat thought about applying machine learning to the huge amount of data they had from NASA’s Solar Dynamics Observatory (SDO) satellite. The Stanford Solar Observatories Group processes and stores 1.5 terabytes of that data per day.
The SDO’s Helioseismic Magnetic Imager was also used to collect vector magnetic field observations.
After taking an online machine learning course at Stanford, Bobra and Couvidat applied what they learnt to their study on solar flares to see if the support vector machine algorithm could provide early warning of the most hazardous types of solar flares: M-class and X-class.
M-class solar flares are medium-large flares that cause minor radiation storms that could endanger astronauts and cause short radio blackouts at Earth’s poles. X-class are the largest flares and can cause a lot more damage.
