Applying machine learning to the universe’s mysteries
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. Image courtesy Berkeley Lab.
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.
The next step will be to apply the same machine learning process to actual experimental data. Powerful machine learning algorithms allow these networks to improve in their analysis as they process more images. The underlying technology is used in facial recognition and other types of image-based object recognition applications.
The images used in this study – relevant to particle-collider nuclear physics experiments at Brookhaven National Laboratory’s Relativistic Heavy Ion Collider and CERN’s Large Hadron Collider – recreate the conditions of a subatomic particle “soup,” which is a superhot fluid state known as the quark-gluon plasma believed to exist just millionths of a second after the birth of the universe. Berkeley Lab physicists participate in experiments at both of these sites.
“We are trying to learn about the most important properties of the quark-gluon plasma,” said Xin-Nian Wang, a nuclear physicist in the Nuclear Science Division at Berkeley Lab who is a member of the team. Some of these properties are so short-lived and occur at such tiny scales that they remain shrouded in mystery.
In experiments, nuclear physicists use particle colliders to smash together heavy nuclei, like gold or lead atoms that are stripped of electrons. These collisions are believed to liberate particles inside the atoms’ nuclei, forming a fleeting, subatomic-scale fireball that breaks down even protons and neutrons into a free-floating form of their typically bound-up building blocks: quarks and gluons.
Researchers hope that by learning the precise conditions under which this quark-gluon plasma forms, such as how much energy is packed in, and its temperature and pressure as it transitions into a fluid state, they will gain new insights about its component particles of matter and their properties, and about the universe’s formative stages.
