NOIRLab/NSF/AURA/M. Garlick
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Artist’s impression of an ultra cool dwarf, with a companion white dwarf.
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Nationalgeographic.co.id—Using old telescope data NOIRLab Source Catalog (NSC) DR2, a citizen scientist named Frank Kiwy has found 34 dwarf star chocolate. Brown dwarfs are relatively cool, faint objects that range in size between gas giant planets, such as Jupiter or Saturn, and sun-like stars.
Sometimes this star is called failed star. That’s because these objects are too small to sustain hydrogen fusion reactions at their core, but they have star-like attributes.
Typically, they have a mass between 11 and 80 times that of Jupiter, and are classified spectrally into M-, L-, T- and Y-type dwarfs. Despite their name, they have different colors.
This star is a mysterious object with more than 12 times the mass of Jupiter and less than half the mass of the sun. Scientists have long debated whether these objects are more like stars or very large planets.
The new detection might help resolve this issue. Even with the best computing technology, humans still have the advantage of finding brown dwarfs in astronomical data, and that’s where citizen science comes in.
“Modern astronomical archives contain an enormous treasure trove of data and often contain major discoveries waiting to be noticed,” said Aaron Meisner, who helped lead the project and is an astronomer at the National Science Foundation’s National Optical-Infrared Astronomy Research Laboratory (NOIRLab) in a statement. .
NASA
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Binary star systems have two stars orbiting around the same center of mass, and are quite common.
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Many brown dwarfs will likely appear magenta or orange-red to the human eye. However, their low mass, low temperature, and lack of internal nuclear reactions make them very faint and difficult to detect.
To help find dwarf star With a new tan in the solar environment, astronomers from the previous ‘Backyard Worlds: Planet 9’ citizen science project have turned to a worldwide network. It is made up of more than 100,000 citizen scientists who examine telescope images to identify the brown dwarf’s subtle motion against a stellar background.
Despite machine learning and supercomputer capabilities, the human eye is still a unique resource when it comes to exploring telescope images for moving objects.
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