By Mary Magnuson
To solve mysteries of the universe, scientists use powerful particle accelerators and detectors. These enormous machines generate millions of particle collisions and petabytes of data every second. The sheer amount of data outpaces the rate at which the detector can transfer it — a growing problem with even more advanced experiments on the horizon.
The U.S. Department of Energy awarded Fermilab engineer Farah Fahim, Ph.D., an Early Career Research Award to work on a solution. She’ll apply the $2.5 million grant to her work, which uses neural networks and machine learning to create efficient hardware for data processing at source. Converting the raw data locally into higher-level, lower-volume information overcomes the data transfer bottleneck. In addition, it enables real-time data-processing, which will accelerate discoveries.
Fahim’s efforts center on the Large Hadron Collider, the 27-kilometer particle accelerator at CERN. There, protons smash together and produce other particles that whiz through a detector. One component, the pixel detector, is the size of a shoebox. It’s layered with 65 million pixels that pick up the particles’ movements. Knowing which pixels a particle touched tells researchers about its trajectory.