Astronomers Discover 116,027 New Variable Stars

by johnsmith

Using machine learning techniques and data from the All-Sky Automated Survey for Supernovae (ASAS-SN) and several other surveys, astronomers have identified 378,861 variable stars, of which 262,834 are known variables and 116,027 are new discoveries.

The variable star Eta Carinae is found within the Carina Nebula. Image credit: ESO / J. Emerson / M. Irwin / J. Lewis.

The variable star Eta Carinae is found within the Carina Nebula. Image credit: ESO / J. Emerson / M. Irwin / J. Lewis.

Variable stars are celestial objects whose brightness waxes and wanes over time, especially if observed from our perspective on Earth.

“In fact, even our Sun is considered a variable star,” said Dr. Collin Christy, an astronomer in the Department of Astronomy at the Ohio State University.

“Surveys like ASAS-SN are an especially important tool for finding systems that can reveal the complexities of stellar processes.”

“Variable stars are sort of like a stellar laboratory. They’re really neat places in the Universe where we can study and learn more about how stars actually work and the little intricacies that they all have.”

In the study, Dr. Christy and colleagues analyzed data from ASAS-SN as well as from ESA’s Gaia mission, the Two Micron All Sky Survey (2MASS) and the AllWISE catalog.

They used a machine learning algorithm to generate a list of 1.5 million candidate variable stars from a catalog of about 55 million isolated stars.

Afterward, they whittled the number of candidates down even further. Of the 1.5 million stars they studied, 378,861 turned out to be real variable stars.

More than half were already known to the astronomy community, but 116,027 of them proved to be new discoveries, including over 111,000 periodic variables and 5,000 irregular variables.

“We plan to incorporate these variables, including the lower probability candidates, into our Citizen Science initiative to help refine our classifications and improve our machine learning techniques,” the astronomers said.

“The citizen scientists outperformed our present machine learning classifier in identifying spurious variables.”

“They also excelled at identifying unusual or extreme variable candidates.”

“This is the first time that we’re actually combining citizen science with machine learning techniques in the field of variable star astronomy,” said Tharindu Jayasinghe, a doctoral student in the Department of Astronomy and the Center for Cosmology and Astroparticle Physics at the Ohio State University.

“We’re expanding the boundaries of what you can do when you put those two together.”

The results will be published in the Monthly Notices of the Royal Astronomical Society.


C.T. Christy et al. 2022. The ASAS-SN Catalog of Variable Stars X: Discovery of 116,000 New Variable Stars Using g-band Photometry. MNRAS, in press; arXiv: 2205.02239

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