Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have developed a novel artificial intelligence (AI)-based method to dramatically tame the flood of data generated by particle detectors at modern accelerators. The new custom-built algorithm uses a neural network to intelligently compress collision data, adapting automatically to the density or “sparsity” of the signals it receives.

Imagine shining a flashlight into a material and watching the light bend backward—or in an entirely unexpected direction—as if defying the law of physics. This phenomenon, known as negative refraction, could transform imaging, telecommunications, and countless other technologies. Now, a team of scientists has managed to use a natural magnetic material called CrSBr to achieve negative refraction—without the need for complicated artificial structures. The study, published in Nature Nanotechnology, opens the door to ultra-compact lenses, super-high-resolution microscopes, and reconfigurable optical devices that can be controlled with magnets.

Applied physicists in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have demonstrated a new way to structure light in custom, repeatable, three-dimensional patterns, all without the use of traditional optical elements like lenses and mirrors. Their breakthrough provides experimental evidence of a peculiar natural phenomenon that had been confined mostly to theory.