Research
Self-learning AI separates particle jets from background noise in simulated heavy-ion collisions
Phys.org ·
The Brief
- A team led by researchers at DOE's Brookhaven National Laboratory developed a self-learning AI approach.
- The AI learns on its own to separate high-energy particle jet signals from background noise in simulated heavy-ion collisions.
- The source describes the work as a simulation-based study.
Why it matters
Separating jet signals from heavy-ion background is a core measurement problem in high-energy nuclear physics, so a method that learns this separation without hand-built rules could matter for analysing collision data.
Who’s affected
- high-energy and nuclear physicists working on heavy-ion collisions
- computer scientists applying machine learning to particle physics data
- #particle physics
- #heavy-ion collisions
- #jets
- #machine learning
- #brookhaven
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…
Timeline
Physics News report on the AI jet-separation study published
Physics News - Physics News, Material Sciences, Science News, Physics
Self-learning AI separates particle jets from background noise in simulated heavy-ion collisions