Fermilab scientists and engineers are developing a machine learning platform to help run Fermilab’s accelerator complex alongside a fast-response machine learning application for accelerating particle beams. The programs will work in tandem to boost efficiency and energy conservation in Fermilab accelerators.
Caitlyn Buongiorno
Caitlyn Buongiorno is a freelance writer in the Chicago area.
Magnets play a key role in looking for the direct transformation of muons into electrons, a theorized phenomenon that Fermilab’s Mu2e experiment will hunt for when it comes online in 2023. In an important milestone, seven essential magnets have passed testing and been accepted for the construction of the experiment.
A collaboration led by Fermilab and Stanford University combines their expertise in quantum science and accelerator technologies to build the world’s largest atom interferometer. The instrument will push the boundaries of quantum physics into macroscopic scales, providing a gateway for dark matter searches and tests of gravitational waves.
Three United States DOE national laboratories – SLAC, Fermilab and Jefferson Lab – have partnered to build an advanced particle accelerator that will power the LCLS-II X-ray laser. Thanks to technology developed for nuclear and high-energy physics, the new X-ray laser will produce a nearly continuous wave of electrons and allow scientists to peer more deeply than ever before into the building blocks of life and matter.
Particle accelerators are some of the most complicated machines in science. In today’s more autonomous era of self-driving cars and vacuuming robots, efforts are going strong to automate different aspects of the operation of accelerators, and the next generation of particle accelerators promises to be more automated than ever. Scientists are working on ways to run them with a diminishing amount of direction from humans.