На информационном ресурсе применяются рекомендательные технологии (информационные технологии предоставления информации на основе сбора, систематизации и анализа сведений, относящихся к предпочтениям пользователей сети "Интернет", находящихся на территории Российской Федерации)

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How a group of students discovered the sounds of solar storms

Author: The Conversation / Source: The Next Web

How a group of students discovered the sounds of solar storms

We are now truly living in the era of big data. And it’s not just companies like Facebook and YouTube that are reaping the benefits, big data is transforming science too.

In the space sciences, we have an unprecedented number of satellites and ground-based instruments that monitor Earth’s space environment – routinely producing tonnes of data.

But how do you process it all? While you may have heard about algorithms and artificial intelligence, there are some decidedly more human approaches too.

One of the things I study are the sounds within the Earth’s magnetosphere – the magnetic bubble that protects us from space radiation. These sounds consist of particles vibrating back and forth.

Because they are electrically charged, they are affected by magnetic fields, but can also themselves affect magnetic fields, such as those from the Earth, to create a magnetic type of sound waves. However, since there are so few particles in space, these sounds are incredibly weak and only present at frequencies thousands of times lower than we can hear.

We can, however, make our recordings of them audible simply by amplifying them and dramatically speeding them up. This latter part can help with big data – you can get through a whole year’s worth of these recordings by just listening to six minutes of audio. And by doing this, we identified the sounds that follow solar storms. Our results were published in Space Weather.

But we didn’t do it alone. One way scientists have been trying to cope with big data is by using citizen science, crowdsourcing volunteers from the public to carry out well-defined tasks that help classify or analyze datasets that would be difficult to do using a computer or a lone scientist.

This has proven incredibly successful. For example, the 1.6m users of the citizen science platform Zooniverse have managed to achieve 375m classifications – ranging from distant galaxies to marine invertebrates.

However, with these…

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