Applying machine learning to the universe’s mysteries

Computers can beat chess champions, simulate star explosions, and forecast global climate. We are even teaching them to be infallible problem-solvers and fast learners.

And now, physicists at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and their collaborators have demonstrated that computers are ready to tackle the universe’s greatest mysteries. The team fed 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 hivelike digital brain in analyzing and interpreting the images of the simulated particle debris left over from the collisions. During this test run the researchers found that the neural networks had up to a 95 percent success rate in recognizing important features in a sampling of about 18,000 images.

The study was published Jan. 15 in the journal Nature Communications (“An equation-of-state-meter of quantum chromodynamics transition from deep learning”).

The next step will be to apply the same machine learning process to actual experimental data.


Image Credit:  Berkeley Lab – 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. 


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