Machines Teach Humans About the Stars

When people think of the stars they usually think about the stereotypical star or the sun. What they don’t think of is the sheer number of stars that our galaxy (let alone our universe) contains and how the sheer mass and number of stars can make studying them incredibly difficult for even the most obsessed and dedicated astrologer. Thankfully, machines and robots are able to accomplish certain things much faster than humans can and they are incredible useful when it comes to sifting through huge amounts of data. Scientists are now using machines like this to help with their study of stars and planets all around the universe.

The use of machines and computers to help scientists shift through data is still relatively new. That being said, it is quickly gaining steam and becoming the norm as computers can go through massive lists much faster and, potentially, notice patterns that would escape the human eye. Normally the search for patterns in stars and planets is accomplished by a spectrum; the sifting of starlight through different wavelengths in an attempt to discover properties. Now machines can use complicated algorithms that sift through data at a much faster and more accurate speed. This allows computer to take information on billions of stars in a relatively quick manner when compared to previous efforts.

After taking the pictures of stars gathered by thousands of telescopes around the world, the computers search through a massive database that show’s all of the pictures of the same stars taken over a period of time. By looking for differences in size, brightness, and other factors, the computers can find new classes of stars as well as track the progress of existing stars as they head towards supernova. This sort of information will become more and more valuable as humanity continues to explore space looking for both evidence of life as well as potential colonizable planets.

If you’d like to read more, the link is here.

from Yaser Khalifa and Evolutionary Algorithms


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