Astronomers Find 116,000 New Variable Stars - by spacewriter
. That gave them a huge database of stars to sift through looking for targets. It also presented a big challenge. “If you want to look at millions of stars, it’s impossible for a few humans to do it by themselves. It’ll take forever,” said Jayasinghe. “So we had to bring something creative into the mix, like machine learning techniques.”
Eventually, between sorting out the JUNK data and running verified information through machine learning, the astronomers had about 400,000 variables to observe. The team turned to the All-Sky Automated Survey for Supernovae telescope network to observe the variable star candidates. The survey’s ground-based telescopes were fitted with blue-sensitive g-band filters to look for the variables.
The JUNK datasets now modify and improve the algorithms in the machine learning program. “This is the first time that we’re actually combining citizen science with machine learning techniques in the field of variable star astronomy,” said Jayasinghe. “We’re expanding the boundaries of what you can do when you put those two together.”It turns out most stars are variables. Astronomers want to know why. Variability is a major clue to activity inside or on the surface of a star throughout its life.
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