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A new study suggests that aging stars may be wiping out the giant planets that orbit closest to them. The research, led by astronomers at UCL (University College London) and the University of Warwick, provides fresh evidence that these planets can be pulled inward and destroyed as their host stars evolve.

Astronomers have long thought that black holes become “active” — spewing out jets of matter and radiation — primarily during their formative years. These periods light up the centers of galaxies and create quasars.

“It is exhilarating to see ‘galaxies’ come out of our computer that look indistinguishable from the real thing and share many of the properties that astronomers measure in real data such as their number, luminosities, colors and sizes,” said coauthor Carlos Frenk, a physicist at Durham University, in a statement about the work.

Hubble Space Telescope AI finds hundreds of never-before-seen 'cosmic anomalies' in old Hubble Telescope images · Dark Universe How astronomers are unveiling the 'skeleton' of the universe

NSF-Simons AI Institute for the Sky (NSF-Simons SkAI) Led by Northwestern University in collaboration with The University of Chicago, the University of Illinois Urbana-Champaign, the University of Illinois Chicago, and the Adler Planetarium, ...

AI reduces errors and accelerates discoveries by processing data more efficiently than humans. This automated approach enables astronomers to focus on analyzing planetary characteristics rather than sifting through raw data.

A global team of astronomers and machine learning researchers today announced the release of the "Multimodal Universe" - a groundbreaking 100 terabyte dataset that brings together hundreds of millions of astronomical observations in unprecedented detail and scale. This massive collection of space data aims to revolutionize how artificial intelligence can be applied to unlock the mysteries of the cosmos.

We’re also excited to be building new connections with the School of Data Science.” · The NSF-Simons Cosmic AI Institute promises a significant leap forward in the technology used in the field of astronomy, harnessing the power of artificial ...

“DeepDISC relies on these AI models that are supervised, which means that to train them, we need some form of pre-labeled information,” Merz explained. This concept, known as ground truth in machine learning, poses a challenge because the project uses real data, such as the locations of stars. “We don’t know exactly where objects are beforehand,” he said. Another issue they encounter is deblending, a process in astronomy that involves differentiating and characterizing light sources in images.

“DeepDISC relies on these AI models that are supervised, which means that to train them, we need some form of pre-labeled information,” Merz explained. This concept, known as ground truth in machine learning, poses a challenge because the project uses real data, such as the locations of stars. “We don’t know exactly where objects are beforehand,” he said. Another issue they encounter is deblending, a process in astronomy that involves differentiating and characterizing light sources in images.