AI tagged posts

An AI-powered tool can distinguish dark matter’s elusive effects from other cosmic phenomena, which could bring us closer to unlocking the secrets of dark matter.

Dark matter is the invisible force holding the universe together – or so we think. It makes up around 85% of all matter and around 27% of the universe’s contents, but since we can’t see it directly, we have to study its gravitational effects on galaxies and other cosmic structures. Despite decades of research, the true nature of dark matter remains one of science’s most elusive questions.

According to a leading theory, dark matter might be a type of particle that barely interacts with anything else, except through gravity...

Researchers from LMU, the ORIGINS Excellence Cluster, the Max Planck Institute for Extraterrestrial Physics (MPE), and the ORIGINS Data Science Lab (ODSL) have made an important breakthrough in the analysis of exoplanet atmospheres.

Using physics-informed neural networks (PINNs), they have managed to model the complex light scattering in the atmospheres of exoplanets with greater precision than has previously been possible.

This method opens up new opportunities for the analysis of exoplanet atmospheres, especially with regard to the influence of clouds, and could significantly improv...

A Yale School of Medicine team reports in a new study that an artificial intelligence (AI) model was able to reliably diagnose people living with Marfan syndrome from a simple facial photograph.

Marfan syndrome is a genetic disorder, affecting about 1 in 3,000 people, which impacts the body’s connective tissues. “Patients living with Marfan syndrome are usually very tall and thin,” said John Elefteriades, MD, professor of surgery at Yale School of Medicine and senior author of the study. “They have long faces and are prone to spine and joint issues. However, many are not diagnosed.”

Marfan syndrome increases the risk for aortic dissection, where the aorta splits suddenly after becoming enlarged...

Astronomers have recently found hundreds of “polluted” white dwarf stars in our home galaxy, the Milky Way. These are white dwarfs caught actively consuming planets in their orbit. They are a valuable resource for studying the interiors of these distant, demolished planets. They are also difficult to find.

Historically, astronomers have had to manually review mountains of survey data for signs of these stars. Follow-up observations would then prove or refute their suspicions.

By using a novel form of artificial intelligence, called manifold learning, a team led by University of Texas at Austin graduate student Malia Kao has accelerated the process, leading to a 99% success rate in identification...