Self-learning AI separates particle jets from background noise in simulated heavy-ion collisions
A team of physicists and computer scientists led by researchers at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory developed a new artificial intelligence (AI) approach that learns on its own to separate important high-energy particle jet signals from the enormous background of...
Image: Phys.org
A team of physicists and computer scientists led by researchers at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory developed a new artificial intelligence (AI) approach that learns on its own to separate important high-energy particle jet signals from the enormous background of particles recorded in relativistic heavy-ion collisions, where two beams of heavy ions collide at near the speed of light.
Originally published at Phys.org
