Prof. Sara Seager, Prof. Robert Langer and Prof. Nancy Kanwisher have been awarded the 2024 Kavli Prize for their work in the three award categories: astrophysics, nanoscience, and neuroscience, respectively, reports Michael T. Nietzel for Forbes. According to the Norwegian Academy of Science and Letters, this award honors scientists with outstanding research “that has broadened our understanding of the big, the small and the complex,” writes Nietzel.
Harrison White '50, PhD '55, “a theoretical physicist-turned-sociologist who upended the study of human relations and society” has died at age 94, reports Michael Rosenwald for The New York Times. “With his background in physics, Professor White viewed humans as nodes within social networks,” writes Rosenwald. “Those networks operated in complex ways that shaped economic mobility, financial markets, language and other social phenomena.”
Prof. Netta Engelhardt talks to New Scientist’s Thomas Lawton about the possibility of singularities existing outside black holes. Theorists can now probe singularities from a deeper perspective, using insights into the possible quantum foundations of gravity. This new approach “flips the script” on how we think about singularities, says Engelhardt.
Prof. Anna Frebel joins Arun Rath of WGBH’s All Things Considered to discuss her recent discovery of some of the universe’s oldest stars, an out-of-this-world identification made the help of MIT undergraduates Hillary Andales, Ananda Santos and Casey Fienberg. “When you meet someone new, you want to know what their name is, how old they are, maybe where they live and what they do, right?” says Frebel. “We do the same with all the astronomical objects in the sky.”
For the first time ever, researchers at MIT have observed electrons form “fractional quasiparticles without enabling the influence of a magnetic field,” reports Daniel Garisto for Quanta Magazine. This discovery “may carry the seeds of long-sought quasiparticles with stable memories that could underpin a new and powerful approach to quantum computing.”
The California Institute of Technology has announced the eight distinguished scientists who will be this year’s Brown Investigators, each receiving up to $2 million over five years to support research on fundamental challenges in the physical sciences, writes Michael T. Nietzel for Forbes. Recipient Prof. Nuh Gedik will, “develop a new kind of microscopy that images electrons photo-emitted from a surface while also measuring their energy and momentum.”
Prof. Seth Lloyd and other physicists have begun to show that in the quantum realm, theoretical routes to the past called time loops might be closer to reality, writes New Scientist’s Miriam Frankel. When first publishing his ideas about quantum time loops, Lloyd says he “probably wouldn’t have done it” given all the questions received about time travel, but now testing time loops is experimentally feasible.
Current AI models require enormous resources and often provide unpredictable results. But graduate student Ziming Liu and colleagues have developed an approach that surpasses current neural networks in many respects, reports Manion Bischoff for Scientific American. “So-called Kolmogorov-Arnold networks (KANs) can master a wide range of tasks much more efficiently and solve scientific problems better than previous approaches,” Bischoff explains.
Researchers at MIT and elsewhere have found that the sun’s magnetic field “could form much closer to the star’s surface than previously thought,” reports Will Sullivan for Smithsonian Magazine. “The findings could help improve forecasts of solar activity that can affect satellites, power grids and communications systems on Earth—and produce magnificent auroras,” explains Sullivan.
MIT scientists are working to fortify coastlines with “architected” reefs that can also provide habitats for fish and marine life, reports Ed Brown for TechBriefs. “We looked at the structure of these reefs and we found some similarities to what we had been doing in fluid mechanics. That led us to the idea of trying to make artificial reefs that we could architect and build in a very directed way,” says Prof. Michael Triantafyllou.
MIT astronomers measured a black hole’s spin for the first time by tracking the X-ray flashes produced by a black hole following a tidal disruption event, reports Interesting Engineering’s Mrigakshi Dixit. “The spin value of a black hole tells us about how it evolved over the age of the universe,” explains Research Scientist Dheeraj Pasham.
MIT astronomers have found a new way to measure how fast a black hole spins, observing the aftermath of a black hole tidal disruption event with a telescope aboard the International Space Station, reports Laura Baisas for Popular Science. “The only way you can do this is, as soon as a tidal disruption event goes off, you need to get a telescope to look at this object continuously, for a very long time, so you can probe all kinds of timescales, from minutes to months,” said Research Scientist Dheeraj Pasham.
Astronomers at MIT and elsewhere have determined how to measure the spin of a nearby supermassive black hole using a new calculation method, reports Isaac Schultz for Gizmodo. The team “managed to deduce a supermassive black hole’s spin by measuring the wobble of its accretion disk after a star has been disrupted—a polite word for torn up—by the gigantic object,” explains Schultz. “They found the black hole’s spin was less than 25% the speed of light—slow, at least for a black hole.”
With the help of undergraduates in MIT’s Observational Stellar Archaeology 8.S30 class, researchers at MIT found three of the oldest stars in the universe orbiting around the outskirts of the Milky Way Galaxy, reports Ava Berger for The Boston Globe. “[The stars] have preserved all this information from early on for 13 billion years for us because they’re just sitting there,” explains Prof. Anna Frebel. “Like the can of beans in the back of your cupboard, unless you crack it open or damage it somehow it just keeps sitting there.”
Researchers at MIT and elsewhere have developed a new machine-learning model capable of “predicting a physical system’s phase or state,” report Kyle Wiggers and Devin Coldewey for TechCrunch.