According to a recent research paper, after analysing telescope data around the world this year, volunteer scientists have successfully identified 10 thousand new variable stars in the Milky Way galaxy.

Volunteers have been examining data from the All-Sky Autopilot Survey for Supernovae since January. The survey is known as ASAS-SN, and is run by researchers at Ohio State University.

In a paper published on arXiv, scientists analyzed the achievements of the "Citizen Science" project, called Citizen ASAS-SN, to date: More than 3100 volunteers provided some 839,000 classification of more than 100,000 light curves), and data revealing to astronomers objects in the sky.

Through this project, volunteer scientists discovered some 10 thousand new variable stars of different species in the Milky Way galaxy.

As their name suggests, variable stars are stars whose luminosity changes over time, and the light we see coming from such stars is not constant, where they abate and then shine again.

Volunteer scientists have classified the newly discovered variable stars as eclipsing bisexuals, with one star passing in front of the other, and pulsating stars and rotating stars.

Together with these various variable stars, the data revealed objects that interfered with the light observed from the stars by telescopes, which the team of volunteer citizen scientists classified as "junk," meaning they were something other than stars.

For example, satellites orbiting LEO can interfere with star light in telescope data and are classified as insignificant.

Volunteer scientists have also identified data that do not correspond to changing star categories as "unknown." Ohio scientists found that volunteers were able to easily identify unwanted data accurately.

This project builds on ASAS-SN's past and ongoing work to search in the sky for black holes and other phenomena in the universe.

The ASAS-SN telescopes recently acquired improvements that allowed astronomers to dig deeper into space in search of new variable stars, supernatural colonies and other things.

The previous analysis of ASAS-SN data was signed using machine learning algorithms, where the data classify the trained computer algorithm.