About 116,000 variable stars have been identified by Ohio State University officials. Their findings in this case are described in the form of a preprint.
This one is available in the website database arXiv It describes how astronomers used ASAS-SN (Automated All-Sky Survey of Supernovae), a network of 20 telescopes around the world. Sky observations have been done for nearly ten years, and the end result can be considered certainly satisfactory.
The data collected by the telescopes was then analyzed using machine learning. It helped discover about 116,000 variable stars. How are these things different from “ordinary” stars? First of all, sudden, distinct changes in brightness that can be observed on a “human” time scale. Although the Sun is relatively stable, it is also considered a variable star.
By looking at these altitudes, astronomers can make important conclusions about the mass, radius, temperature, and even composition of a particular star. The study authors compare variable stars to space laboratories. By observing them, you can gather information about how the stars work and develop.
In total, 116,027 new variable stars have been identified
How were so many variable stars discovered? In 2018, ASAS-SN decided to use G-band filters capable of detecting more types of blue light. As a result, astronomers have made a huge leap from being able to track 60 million stars at once to more than 100 million. Ultimately, Colin Christie and his colleagues used a machine learning algorithm to generate a list of 1.5 million candidates for variable stars.
Of this group, excluding some candidate elements, approximately 400,000 objects have been considered variable stars. Some of them have already been classified as variable stars, but it turns out that 116,027 new discoveries are unknown to science. Further development of this type of method should lead to more items being added to the list in the future.
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