Social networking algorithms help in study of space weather, substorms

Scientists have revealed the global nature of magnetospheric substorms, the space weather phenomena responsible for the Northern Lights.

The discovery was made possible by the same algorithms that help social networking sites match like-minded friends.

Researchers amassed data on disturbances in the Earth's magnetic field collected by magnetometers positioned all over the Northern Hemisphere.

Using the social networking algorithms, scientists were able to find links between disturbances, revealing the true scale of auroral substorms.

According to the study's findings, published Tuesday in the journal Nature Communications, substorms reveal themselves as global-scale electrical current systems, spreading across more than a third of the planet's upper atmosphere.

When charged particles streaming from the sun are subsumed by Earth's ionosphere, the inner boundary of the magnetosphere stores energy like a battery.

Solar storms deliver lots of charged particles, which cause ionosphere to release its stored energy and trigger electric currents that influence Earth's magnetic fields. Magnetometers on the ground can measure these disturbances.

For the study, scientists gathered magnetometer recordings of 41 known substorms that struck between 1997 and 2001.

The same algorithms used to link like-minded friends or queue up targeted online advertisements showed substorms first present as small communities of electrical disturbances, but over time, these communities reveal themselves to be part of a large correlated system.

"We used a well-established method within network science called community detection and applied it to a space weather problem," lead researcher Lauren Orr said in a news release.

"The idea is that if you have lots of little subgroups within a big group, it can pick out the subgroups," said Orr.

Orr conducted the study while earning her doctoral degree in physics at the University of Warwick, in Britain, but now works as a research associate at Lancaster University.

"We applied this to space weather to pick out groups within magnetometer stations on the Earth" Orr said. "From that, we were trying to find out whether there was one large current system or lots of separate individual current systems. This is a good way of letting the data tell us what's going on, instead of trying to fit observations to what we think is occurring."

The authors of the new study suggest their findings can help scientists build for accurate space weather forecasting models.

"Our research introduces a whole new methodology for looking at this data," said study co-author Sandra Chapman. "We've gone from a data poor to a data rich era in space plasma physics and space weather, so we need new tools."

"It's a first to show that you can take one of these tools to our field and get a really important result out of it. We've had to learn a lot to be able to do that, but in doing so it opens up a new window into the data," said Chapman, a professor at Warwick.

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