The colored lines represent calculated particle tracks from particle collisions occurring within Brookhaven National Laboratory's STAR detector at the Relativistic Heavy Ion Collider, and an illustration of a digital brain. The yellow-red glow at center shows a hydrodynamic simulation of quark-gluon plasma created in particle collisions.
Particle colliders enable us to probe the fundamental nature of matter by observing exotic particles produced by high-energy collisions. Because the experimental measurements from these collisions are necessarily incomplete and imprecise, machine learning algorithms play a major role in the analysis of experimental data. The high-energy physics community typically relies on standardized machine learning software packages for this analysis, and devotes substantial effort towards improving statistical power by hand crafting high-level features derived from the raw collider measurements. In this paper, we train artificial neural networks to detect the decay of the Higgs boson to tau leptons on a dataset of 82 million simulated collision events. We demonstrate that deep neural network architectures are particularly well-suited for this task with the ability to automatically discover high-level features from the data and increase discovery significance.
Computers can beat chess champions, simulate star explosions, and forecast global climate. They are also being trained as infallible problem-solvers and fast learners. And now, physicists at the U.S. Department of Energy's Lawrence Berkeley National Laboratory and their collaborators have demonstrated that computers are ready to tackle the universe's greatest mysteries. The team used thousands of images from simulated high-energy particle collisions to train computer networks to identify important features. The researchers programmed powerful arrays known as neural networks to serve as a sort of hive-like digital brain in analyzing and interpreting the images of the simulated particle debris left over from the collisions.