Using machine learning techniques, a team of astronomers discovered “quadruples,” or quasars.
A recent study, which took only 1.5 years, increased the number of known quasars by 25% thanks to machine learning. Previously, 50 similar objects were opened.
The researchers used data from the WISE telescope and then from Gaia to overlay the findings. They then used machine learning tools to determine which quasars were quasars and which ones were mistakenly listed.
Subsequent observations with the Keck Observatory Low Resolution Imaging Spectrometer (LRIS), as well as the Palomar Observatory, New Technology Telescope, and Gemini-South confirmed which of the objects were indeed quasars.
The authors are studying this type of quasars because they can help determine the expansion rate of the universe and answer questions about dark matter.
New quadruple quasars, called, for example, “wolf’s paw” and “dragon snake”, will help in future calculations of the Hubble constant. Due to their location, quasars give astronomers the opportunity to explore the intermediate range of the universe.
The authors reply that they will continue to use and improve AI, but it will not replace the people who make decisions.