Caitlyn Buongiorno

Caitlyn Buongiorno is a writer and social media manager in the Fermilab Office of Communication.

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Engineers and scientists at Fermilab are designing machine learning programs for the lab’s accelerator complex. These algorithms will enable the laboratory to save energy, give accelerator operators better guidance on maintenance and system performance, and better inform the research timelines of scientists who use the accelerators. The pilot system will used on the Main Injector and Recycler, pictured here. It will eventually be extended to the entire accelerator chain. Photo: Reidar Hahn, Fermilab

Fermilab receives DOE funding to develop machine learning for particle accelerators

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.

MAGIS-100: Atoms in free fall to probe dark matter, gravity and quantum science

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.